In this section, we give a breakdown of our findings in general. A typical economic loss evaluation includes three important calculations: (1) a loss of earning capacity, (2) a loss of value of household services an individual can perform, (3) and a loss of quality of life. In general, our baseline is the present expected value of what a plaintiff would have earned (income and fringe benefits) had he or she not been injured. We use forecasting to predict how earnings would likely change as a person ages. In doing so, we compute a plaintiff's likely post-injury earning capacity. Since majority of the time a plaintiff's post-injury prognosis is unclear, we use statistics to estimate post-injury earnings by using the information contained in enormous databases (ex. Current Population Survey) to forecast potential earnings for many possible scenarios, although virtually always for the scenario where a plaintiff is considered temporarily, partially, or permanently disabled.
Many of us prefer to direct our attention to the results first before digging into the lengthy explanation. Consequently, we use this section to briefly explain the results located on the first page of the supporting tables (page 12 in this report) which sums up majority of the findings throughout our analysis. In this section, we tend to reiterate the facts of the case (ex. demographics, dates of importance, etc.) as we understand them and summarize our findings.
Lost earnings are calculated by finding the difference between what a person would have made, had they not been injured, and what they are projected to make due to their injury. That being said, this section explains the calculation of the first half of the equation. To summarize, we distribute a person's hazard probability across each year of their potential life. These hazards include: mortality, disability, non-participation in the labor force, and unemployment. We then take a statistically average income obtained from numerous observations of other workers in a similar, if not identical, trade. We then derive the percentage change at each age of those projected statistical earnings and apply them to a plaintiff's actual personal income reported by their W-2's or other verifiable means.
In the state of Nevada, because of the precedent set by the Banks v. Sunrise Hospital case, calculating estimated lost quality of life damages is allowed. There are two ways we generally see economists use to calculate lost quality of life damages: (1) the willingness to pay approach and (2) the whole time approach. Dr. Carroll prefers to use the whole time approach for reasons noted in this report. In the whole time approach, we derive the opportunity cost (or value) of a person's leisure time by looking at the difference in value between the number of hours a plaintiff worked on average and the number of hours that he or she could have potentially worked had they decided to cash in all their leisure time and work instead.
Here is the explanation for the other half of the equation. Like we mentioned before, lost earnings are calculated by taking the difference between a plaintiff's pre-injury earning capacity and their after-injury earning capacity. In this report, you will find two different possible scenarios laid out for the plaintiff: (1) their after-injury earning capacity should they become temporarily disabled after their injury and (2) their after-injury earning capacity in the case they are partially permanently disabled. This will help a jury decide on an appropriate amount of damages to award once they rule on what they believe is the plaintiff's fate will be. Rarely do we see cases where we are certain of a plaintiff's fate. Therefore, it is in the best interest of all parties involved that we lay out the different likely scenarios.
The life expectancy section of the report details how we calculate a plaintiff's life expectancy. Using a table showing of the amount of deaths per 100,000 for plaintiff's race at each age, we are able to calculate the probability of survival. A person's life expectancy is the number of years the person is projected to live, which is taken from a calculation of the exact age where the probability equals 50%. It is important to take into consideration what life expectancy refers to. Many times, experts calculate earnings based on a 100% chance of survival through the plaintiff's expected life expectancy. We adjust for risks at each age of a plaintiff and in doing so, we are able to provide reliable forecasted numbers.
In this section, we calculate an estimated value of household services. The value household services is the amount a plaintiff would have to pay to hire someone to perform the services they would have otherwise done themselves had they not been injured. Again, we first calculate a pre-injury figure to compare against a post-injury figure in order to derive the value of the lost household services. As we normally do, we provide a calculation for both possible scenarios, becoming temporarily or partially permanently disabled.
Here, we make the case that statistical forecasting is the most appropriate way to calculate a plaintiff's lost earnings. Our approach is different than majority of our competition. We are only interested in providing estimated economic losses for all the likely scenarios once a jury decides liability in a case. We are in the business of assisting a jury make to make their decision of "how much" by drawing out multiple scenarios and reporting what a person would likely need to recuperate their potential losses they might incur after their injury. Using statistics, both properly and responsibly, is the most logical way to achieve this.
Without regression analysis, these reports would not be possible. A regression equation helps us forecast which independent variables (ex. age, ethnicity, number of children, etc.) is likely or not likely to affect a dependent variable (ex. likely number of hours of work per week, likely earnings due to a disability, likely earnings for a particular trade) and to what degree. Regression analysis is the go-to tool for econometricians. It is used for numerous applications throughout the world and is a efficient method of calculating "the norm."
The remainder of this report serves as the supporting documentation for the figures we calculate. Here, we show the likely expansion and retraction of statistical earnings as we age(p. 14-17), the regression equations we use to calculate the statistical average of earnings for a statistically similar individual (p. 30-39), tables defining those variables used in our regression analysis (p. 20-29), and a table summarizing all the facts of the case and projecting economic losses(p. 12). That is not everything, of course, so we encourage you to take a look at all the tables in order to capture a general idea and what we do in our reports.