Know Your Worth

By Ishi Shrivastava, Kamala Nehru College, University of Delhi

I wonder sometimes, whether people, in their daily lives, ponder over the various functionalities that help us exist as humans and citizens of a particular country. How the worth of a country, in an overview, is reliant on my income as a citizen, and how I place my faith in the system to take into account my personal life before assessing the country’s human capital or even formulating the annual budget. And when I place my faith in the system, I also am hopeful that the judicial system will come to my rescue if I am faced with injustice.

How do I know if details of my income or personal life will be taken into account when I am standing in the court of law?

That is when forensic economics comes into play. Conceived of as economics applied to legal matters, forensic economics assesses major issues regarding economic damages in the context of litigation. It is a broad discipline that applies economic theories and methods to matters subject to legal review. A forensic economist creates an alternative world with assumptions, wherein a particular citizen is not faced with injustice, and assesses the worth of economic reparation by grounding on facts (that include income and personal life of the citizen) as well as economic and statistical trends.

To understand better where forensic economics can be applied in a citizen’s daily life, we will use the most widely accepted defining factor of an individual’s life, that is, work.

The economic worth of a citizen can be found out by measuring the citizen’s human capital. To be able to measure that, one requires to estimate the work-life expectancy of an individual. Now the question is: what is work-life expectancy? As the word itself suggests, it is the number of years an individual is expected to be in the workforce. The factors that affect each individual’s work-life expectancy include educational attainment, race, marital status, parental status, and, most importantly, gender.

Economists resort to the approach of forensic economics to be able to measure an individual’s work-life expectancy, which is contingent on the underlying factors of the individual’s personal life. Under this, they establish the fact that work is not only restricted to having paid work, but also unpaid non-market work, which holds a certain economic value. Therefore, “total work-life expectancy” is defined as a sum of market work and nonmarket work.

Now, let us establish two imaginary individuals to make our observation simpler: Sheila, a woman, and Joel, a man.

Gender as a factor of their “total work-life expectancy”: the idea that taking care of a home or family is actual work is uncontested and women lead men in that work activity. The specific reason for people not participating in the market work is taking care of the household or family. Let’s consider here that both Joel and Sheila are between the ages 18 to 24. Using the approach of forensic economics, it is found out that Joel takes care of his household or family the maximum under this age group and the percentage slowly declines with increasing age. Conversely, Sheila taking care of a home or family rises as a percentage under this age group to a maximum at ages 30 to 34 and then slowly declines by age. Therefore, regardless of other factors, a 25 year old Joel has about 35 years of market work-life and 1 year of non-market work-life remaining, whereas Sheila has about 30 years of market worklife and 6 years of non-market work-life. Both of their “total work-life expectancy” equals 36 years but the difference lies in the market and non-market work based on their gender.

Race, education and marriage as factors of their “total work-life expectancy”: the factors of race, education and marital status of an individual are inherently intertwined in determining their work-life expectancy. Let us assume that Sheila is a 30 year old white woman and X is a non-white woman. When both Sheila and X are single women with an educational qualification lower than a high school degree, Sheila’s work-life expectancy exceeds X’s by only a year; the same applies to married white and non-white women. Conversely, if both Sheila and X are employed and have high school degrees, the work-life expectancy of Sheila will exceed X’s by over two years. Now, if we consider Joel to be a 30 year old white man and Y to be a non-white man, we get different results. When both Joel and Y are single men with an educational qualification lower than a high school degree, they get the same result as Sheila and X, that is, one year; and the same holds for married men. But, if Joel and Y (both single) have high school degrees, then the work-life expectancy for Joel exceeds Y’s by around 4 years; and by 3.5 years if both men were married. This acute difference between white and non-white men is essentially because men tend to face more discrimination on the basis of their race.

There exists a negative relationship between marriage and the work-life expectancy of women. Generally, women from all races face a setback in their market work participation post marriage. Women with higher degrees face a setback larger than women with a lower or no degree. This is largely because of two reasons – (a) women tend to be more productive in the non-market or household work, and (b) women have fewer dilemmas in terms of opportunity cost because women already get lesser pay in comparison with their male counterparts. This leads me to establish the fact that the relationship between marriage and work-life expectancy for men is essentially positive. The work-life expectancy of a married white man exceeds that of an unmarried white man by about 3 years; and a married non- white man has a work-life expectancy of about 4 years more than an unmarried non-white man.

Now, we consider the parental status as a factor of the “total work-life expectancy”: Sheila wants to bear a child, but she also wants to keep her work-life expectancy high. To achieve that, she turns to forensic economics and realizes that the work-life expectancy of a single mother exceeds that of a married mother by two to three years. Similarly, Joel realizes that a married man’s work-life expectancy changes negligibly in the case of a child but changes considerably if he chooses to be a single father.

Having analyzed the most basic underlying factors pertaining to the work-life expectancy of individuals, it is imperative to establish the fact that the aforementioned observations were made using the Markov Increment Decrement Model (MID) under Forensic Economics, which gives us the conclusion that when the productivity of non-market work is recognised, we realise that the work-life expectancy of women is negligibly different than that of men, which busts the myth that men are more productive than women.

Therefore, we come to where we started off : the worth of a citizen, which is measured only after taking into account what I, as an individual citizen, possess with respect to my personal life.

And hence, the wonderment ends.