Jorge Gallardo-García’s work has focused on mass torts cases since he joined Bates White. That work has included numerous product liability engagements as well as bankruptcies, insurance coverage disputes, and financial reporting. Here he discusses some strategies to solve problems of forecasting liabilities or damages in cases and illustrates how those can apply to other types of work.
Q. Bates White provides estimates and forecasts for product-related liabilities in many of its mass torts cases. What is your general approach to this work?
A. The first step should be to understand the source of the issue and what generated it. Understanding the mechanisms that generate the process and how those mechanisms may change over time would be next. For example, to estimate current and future product liabilities, one must understand how the product was used, when it was used, and where it was used.
These facts determine the population that may have had contact with the product. Identifying the population allows one to analyze its characteristics and how those characteristics will evolve in the future (e.g., age or gender distribution). These analyses inform the pool of potential claims. However, not all potential claims contact lawyers and file claims. One must understand the mechanisms and frequency with which potential claims contact plaintiffs’ counsel and what features in the tort system affect the flow of such contacts.
Finally, one values the expected claims, considering the specific features of the litigation, such as availability of codefendants, the nature of alleged injuries, and litigation costs. Understanding how these features may or may not change in the future determines the breadth of potential future costs. Often, it’s tempting to just look at the history of a situation and assume that it will continue in the same way in the future. That may or may not be correct. Instead of simply assuming, one can evaluate that assumption by understanding the underlying process, as I explained above.
Q. Can you give an example?
Sure, a simplified example illustrates the approach. Imagine a case in which claims stem from a specific jobsite that closed years ago. If you know the number of people who ever worked at the jobsite, you could have a good idea of the size of the population. Knowing the age and gender of people in that population would allow you to estimate how many may remain alive, as well as the future incidence of alleged disease. Applying the specific incidence rate to the demographic characteristics of the at-risk population allows you to determine how many claims to expect in the future because, after all, the population is fixed.
In contrast, if you just looked at, say, the average number of claims per year and assumed that the same number of claims will be filed in future years, you could be overestimating or underestimating the number of future claims. For example, if the alleged disease occurs disproportionately at younger ages, then one would expect claims to decline in the future. In contrast, if the alleged disease occurs disproportionately at older ages, then one would expect claims to initially increase, as the at-risk population aged, then decline as the at-risk population succumbed to general mortality.
Again, this is a simplified, stylized example. Most of my matters require more complex analyses. My approach parallels that of a good doctor: an economist should find and understand the underlying cause of a symptom instead of just treating the symptom. Doing so results in strong, reliable analyses.
Q. What technical capabilities do you apply to this type of work?
My analytical and statistical background allows me to apply rigorous economic and statistical principles to understand the situation that underlies an empirical observation in my work. In my experience, different cases or circumstances may call for different economic or statistical techniques.
For instance, say I observed an increase in clams in a tort. Interesting questions could be: Is this increase permanent or transitory? Has the increase subsided or could it continue? To answer these questions, I analyze the mechanism that generates the claims, whether certain events happened recently, and the nature of such events. This analysis may use concepts from the law and economics literature, applied theory of incentives, applied econometrics, statistics, or a combination thereof.
Economics also allows me to analyze the incentives that different parties in the litigation might have and how that affects future claims. Data analysis, statistics, and econometrics can help create a range of possible scenarios of future claims, to facilitate understanding the scope of the issue.
Q. You have expertise in estimating liabilities in the context of bankruptcies. Can you share any specifics that illustrate your approach to this kind of analysis?
A. Estimating liabilities in bankruptcy represents a specific application of what I just described. I strive to understand the source of the liability and its scope so I can provide a reliable range of potential estimates. An analysis within a bankruptcy often requires an estimate of “all” current and future liabilities related to a product, so they can be considered as part of a plan of reorganization. This may call for preparing forecasts that span decades into the future. Thus, I prepare robust analyses with sound assumptions that account for the potential uncertainty around them.
Q. The firm also does financial reporting. Do you apply the same skills and techniques to this kind of work?
Financial reporting represents another specific application of what I described. We typically update our financial reporting estimates annually. This means one has the opportunity of periodically reviewing assumptions and incorporating additional information as it becomes available. This contrasts with bankruptcy, in which we typically present estimates only once for a confirmation hearing based solely on the information available at that one point in time.
Q. Does any case you’ve worked on stand out in your mind?
A. The Garlock Sealing Technologies bankruptcy stands out for me. I testified in the estimation trial in 2013. We performed interesting economic and econometric analyses in that case that advanced the state of the art for liability estimation.
Part of the analysis explained the relationship between settlements and liability. To do this, my team relied on well-established models from the law and economics literature. One interesting aspect was that we constructed a rich database to apply the model and empirically determine the difference between Garlock’s liability and settlements in the tort system.
As I mentioned before, often it’s tempting to assume past is prelude to future and simply extrapolate historical expenditure patterns into the future. But it’s important to question how strong that assumption is and to ask whether the situation provides the foundation for better assumptions. The analysis in the Garlock matter demonstrated the importance of understanding the processes underlying the generation of claims, instead of simply extrapolating historical values. Also, I am very proud of that case because Judge Hodges adopted our estimate and had such positive comments about our analysis.
