As efforts to contain the epidemic enter a critical stage, it is important to remember that the costs cannot be measured purely in economic terms, as the measures are taken will have implications for life expectancy across the entire nation.
Analysis of historical data from various countries gives insight into the relationship between life expectancy and GDP per capita. In the first place, it is clear that countries with higher per capita incomes have longer life expectancies, owing to the ability and willingness of wealthier nations to invest in healthcare, infrastructure, and environmental governance, thereby increasing life expectancy and reducing fatality rates.
Research suggests that, in general, a 100% increase in per capita income under similar conditions equates to an increase in life expectancy of 1-3 years. Over the past few decades, with the continued increase in per capita income in China, life expectancy has steadily increased in tandem. On the basis of this, we can make a conservative estimate that a 50% decrease in GDP would see a 1.5-year decrease in life expectancy. Thus, for each 1% reduction in GDP, life expectancy will decrease by approximately 10 days.
This hypothesis can be tested against the economic theory of the “value of life”. In the realm of economics, the “value of life” is a relatively mature concept that refers to the amount that a society is willing to spend in order to increase the average life expectancy. Some will deem the notion of calculating a value for life to be cynical or even repulsive, as life is priceless. From an ethical point of view, this is entirely correct. In reality, however, whether in terms of work, business, or social management, a balance must be struck between reducing the risk of fatality and the cost of doing so. In order to identify this balance, a value for life must be calculated in a scientific, if seemingly ruthless, manner.
For example, some jobs inherently entail a far higher risk of fatality than others, such as underground mining and construction of ultra-high buildings. From the perspective of purely reducing the risk of death, these jobs should be eliminated. But in reality, doing so would both increase the unemployment rate and have adverse impacts on the natural progression of related work, and ultimately, society as a whole will bear the cost of underdevelopment. In this case, a more rational approach would see the introduction of stronger labor protections for such jobs. Finally, with an income premium determined by the market, high-risk jobs would be rewarded with higher salaries, and an acceptable balance may be achieved.
Similarly, enterprise and government must strike a balance between risk and cost in the provision of transportation infrastructure. For example, in designing a new road, governments can reduce the number of fatalities through the implementation of safety provisions, like extra lanes, non-motorized lanes, and wider sidewalks. Evidently, however, not all roads are built in this way. Does this mean that the designers of those roads had a disregard for safety? Of course, this is not the case. Even if the proposed road is designed to be impeccably safe, should the cost be RMB 10 billion (approx. USD $1.4 billion), it is likely that the road will not be built at all, leaving people with no transportation infrastructure. Thus, for such construction projects, the government will issue minimum standards for safety, but it is up to the designer to determine the upper limit.
So, how much is a reduction in fatality worth? In determining this, an implicit calculation is made to strike a balance with the value of life. In fact, economists have long calculated the value of life in economic terms based on data from various countries. Generally speaking, the value of life in developed nations is between 10-100 times the GDP per capita.
Assuming that the value of life is calculated at 30 times the GDP per capita, the average life expectancy would be around 80 years or approximately 30,000 days.
This inference can be tested by comparing the GDP per capita and life expectancy of different countries.
In terms of preventing and controlling infectious diseases, with reference to influenza numbers from previous years, in the absence of large-scale compulsory quarantine measures, the infection rate will not exceed 10% of the overall population, and the fatality rate will be around 0.2%. Thus, the total number of fatalities relative to the entire population will be 2 in 10,000 (0.02%). Assuming that the life expectancy of those who die of influenza is around 60 years, and the average life expectancy across society is 80 years, each person who has died of influenza will have died prematurely, on average, by 20 years. Calculating on the basis of the fatality rate of 2 in 10,000 (0.02%), the per capita reduction in life expectancy will be 20 multiplied by 0.02, which is four-thousandths of a year, or about 1.5 days. Therefore, on average, the impact of a mass-scale influenza outbreak on human society is a reduction in life expectancy of 1.5 days.
On the basis of this analysis, it is possible to infer a reasonable social policy. If every person infected with influenza, that is, 10% of the population, is quarantined for 14 days, and family members who have been in close contact with them (assuming 20% of the population) are also be quarantined, the loss to GDP due to their inability to participate meaningfully in the creation of wealth for this period will be 30% * 14/365 = 1% of GDP. As mentioned above, a 1% GDP regression will cause retrogression across society in medical care, infrastructure, and environmental governance, amounting to a reduction in the average life expectancy of about 10 days, a number far greater than the impact of influenza. Based on this calculation alone, pure isolation is not an effective means of containing influenza, and thus no country or society will implement such measures.
Some may deem the above calculation to be alarmist, but in actuality, this does not even take into account the formidable operation costs of isolating so many people or the costs of restriction population movement. A less optimistic estimation of the losses incurred could be 10% of GDP, or even higher, leading to a reduction of the average life expectancy by 100 days or more, possibly amounting to a loss of life equivalent to dozens or hundreds of times the number of deaths attributable to influenza itself.
Of course, if quarantine measures are able to isolate the flu at an early, small-scale stage, for example, 1% of the population, or within one or two cities, then such measures can still be effective. Once infections spread to over 10% of the population, however, the continued isolation of patients and people in close contact with them will amount to a greater overall toll on lives.
The present epidemic is distinct from previous influenza outbreaks, and therefore, factors such as mortality, the rate of infection, and the proportion of people who need to be quarantined are different, and a significant amount of data is yet to be observed. The same logic, however, applies to the impact of the economy on life expectancy.
Society has established its determination to beat this epidemic, and such an attitude is undoubtedly correct and necessary, and ultimately, this victory will belong to the entire human race. However, I also hope that as society strives to beat this epidemic “at all costs”, the above analysis can help society to keep various “costs” to a minimum.
We must adopt a scientific and rational attitude in determining the most appropriate means of controlling and eradicating the epidemic. In responding to the novel coronavirus, cancer, cardiovascular disease, and other diseases that threaten lives, we must also give comprehensive consideration to social and medical resources, and strike a balance that is conducive to protecting lives. Regularity and security in everyday life and work is an important and fundamental part of life for every person, and we should strive to minimize the impact on this.
*James Liang is the Co-founder and Executive Chairman of Trip.com Group and professor at Peking University’sGuanghua School of Management. The opinions expressed here are entirely his own.
