Know Your Worth: Work-Life Expectancy From the Lens of Forensic Economics

I wonder sometimes whether people, in their daily lives, ponder over the functionalities that help us exist as citizens. How does the worth of my country depend on my income? I place my faith in the system to consider how various decisions will affect me before assessing the country’s human capital or even formulating the annual budget. And when I place my faith in the system, I am also hopeful of the judicial system to come to my rescue if I am faced with injustice.

But how do I know if the 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 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 which are subject to legal review. A forensic economist creates an alternative world with assumptions, wherein the particular citizen is not faced with injustice.

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 worth of a citizen, economically, can be found out by measuring his/her human capital. In order to measure that, one is required 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 education attained, 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 restricted to being involved in only paid employment, but also unpaid non-market work, which also holds certain economic value, thereby, defining ‘total work-life expectancy’ as a sum of market work and non-market work.

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 household or family is actual work is uncontested and women lead men in that activity. Many times the reason people usually cite for not participating in market work is taking care of household or family. Let’s consider here that both Joel and Sheila are between the ages of 18 to 24. Using the approach of forensic economics, it is found out that the maximum number of men who take care of their households belong to the age group of 18 to 24. However, as we start moving up the total percentage of men participating in non-market work reduces. On the contrary, the percentage of women participating in non-market work starts rising from 18 to 24 and reaches its peak in the age group of 30 to 34, after which it starts to decline. Therefore, a 25-year-old Joel would have about 35 years of market work-life and 1 year of non-market work-life remaining, whereas a 25-year-old Sheila would have about 30 years of market work-life and 6 years of non-market work-life. Both their ‘total work-life expectancy’ would equal 36 years but the difference lies in the market and non-market work based on their gender. This conclusion has been drawn from the Markov Increment Decrement Model (MID).

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 his/her work-life expectancy. Let us assume that Sheila is a 30-year-old white woman and X is a non-white woman. If both, Sheila and X, have educational qualifications of less than a high school degree then, Sheila’s work-life expectancy will exceed that of X by only a year. Conversely, if both Sheila and X are employed and have high school degrees, the work-life expectancy of Sheila will exceed that of X 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. If both, Joel and Y, have educational qualifications of less than a high school degree, they get the same result as Sheila and X. However, if Joel and Y (both single) have high school degrees, then the work-life expectancy for Joel exceeds that of Y 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 of all races face a setback in their market work participation post-marriage. Women with higher degrees face a larger setback than those with lower or no degree at all. This is largely because of two reasons– (a) women tend to be more productive in non-market or household work, and (b) women have a lesser dilemma to face in terms of opportunity cost because women already get lesser pay in comparison to 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.

Parental status as a factor of their ‘total work-life expectancy’: Let us suppose that 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 realises that the work-life expectancy of a single mother exceeds that of a married mother by 2-3 years. On the contrary, Joel realises 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 analysed the most basic underlying factors 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. This brings us to the conclusion that when the productivity of non-market work is recognised, 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 is measured only after taking into account what I, as an individual citizen, possess with regard to my personal life.

And hence, the deliberation ends.


Ishi Shrivastava

I am an inspired reader and writer who built this inclination since school days and took part in editorial teams to hone my skills as well as interest. I am also little bit of a good conversationalist.

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