Quantifying economic harm is predicated on establishing a causal relationship between the conduct at issue and an outcome of interest. Often, it is difficult to obtain experimental data to assess this relationship. An event like COVID-19 offers an opportunity to obtain quasi-experimental data for this purpose. Temporal and geographic variation in the incidence of the pandemic, and in policy responses to it, provide natural experiments to assess economic harm and damages.
To quantify the economic harm attributable to a conduct at issue, it is necessary to assess the causal link between the conduct and economic outcomes of interest such as market shares, sales revenues, profits, or royalty payments. To this end, economists typically adopt a but-for paradigm where the outcomes of interest are measured and compared with and without the conduct at issue. Defining and quantifying the outcomes of interest in a scenario without the conduct at issue can be challenging.
Economists may establish the causal relationship between explanatory variables and an outcome of interest through laboratory and field experiments. For example, in laboratory experiments, participants are randomly assigned to control and treatment groups. Random assignment is aimed at eliminating any systematic differences between the two groups other than the treatment itself, so that different outcomes between the control group and experimental group can be attributed to the treatment. Field experiments provide evidence by using a similar experimental design but implemented in the real world. For example, employment discrimination may be assessed through audit studies where success in applying for job vacancies is analyzed for selected pairs of applicants that differ only in their race, color, religion, sex, national origin, age, disability, or genetic information.
Generating laboratory or field experiments to measure a causal relationship is difficult, particularly in the context of litigation. Economic phenomena often are not amenable to laboratory experiments, and field experiments can be costly, complex, and time consuming to implement and evaluate. An occurrence such as COVID-19 and policy responses to it can act as natural experiments from which to test and measure the impact of an alleged conduct on economic outcomes. Rather than relying on a laboratory or a field experiment in which an intervention is implemented by design, actual world occurrences can provide an analytical empirical paradigm analogous to a control-treatment experiment. Naturally occurring random differences in exposure to a government policy or other exogenous events define a treatment and a baseline (or control) group. Observational data on the outcomes for each group can be collected to quantify economic effects.
For example, a May 2020 ruling by the Wisconsin Supreme Court abruptly ended the “Safer at Home” order by the Wisconsin Department of Health Service. This court-mandated lift of statewide COVID-19 shelter-in-place orders in Wisconsin has been used to assess the economic and health effects of these orders. Observational data to measure the effects of lifting shelter-in-place orders typically would only be available when certain thresholds are met indicating low hospitalization or low contagion rates. The Court’s decision offers a unique opportunity to collect data where shelter-in-place orders are lifted but thresholds are not met. That is, evidence can be collected from instances where the lifting of shelter-in-place orders is exogenous and not dependent on the incidence of the pandemic. Counties in Wisconsin subject to this change constitute a treatment group. Data from counties in other states where shelter-in-place orders remained in place provide a control group to measure the impact of lifting shelter-in-place orders.
COVID-19 and policy responses to it have disrupted most areas of economic activity and have led to economic disputes. Yet, the variance in spread and different policy responses also provide opportunities to gather reliable empirical evidence from which to address damages questions. In particular, the behavioral response of interest at the center of an economic dispute may be examined by exploiting temporal and spatial variation in the spread of the pandemic and differences in responses over time and geography. For example, employees across the country have filed economic damages claims against employers based on alleged negligent responses to the pandemic. Because policy responses vary across counties for reasons unrelated to the incidence of the pandemic, it is possible to rely on these natural experiments to assess the effect of the alleged negligent conduct on worker health. Employees in counties that relax safety measures for the business at issue in accordance with state mandates constitute a treatment group. Employees in counties that are categorized as comparable in terms of the incidence of the pandemic but choose not to relax safety measures for the business at issue provide a control group. To the extent that the two groups of employees differ only in the application of safety measures by their employers, the exogenous variation in safety measures across counties allows for measuring their effect on employees’ health.
To rely on a natural experiment as a tool for drawing causal inferences, it is necessary to show that the treatment group in a naturally occurring experiment is effectively randomly assigned. Such random assignment implies that the only systematic difference between the control and treatment groups is the treatment itself. Thus, caution must be taken when extrapolating the results of the effects on the treatment group to the total population. Even in an ideal randomized trial, imbalances in the profile of the participants of each group can be present by chance. This is exacerbated in natural experiments where the assignment to the control and treatment groups is not random.
In sum, economists typically rely on empirical evidence to assess damages causation and quantify damages. Sometimes, however, it is challenging to identify suitable data for this purpose. Natural experiments offer an alternative source of data to test and measure damages. COVID-19 and policy responses to it produce observational data that approximates empirical evidence that could only otherwise be generated from an experimental design.