Why People and Companies Die While Cities Keep Growing
The Winter issue of strategy+business includes a very interesting article on theoretical physicist Geoffrey West, - The Fortune 500 Teller. Dr. West is Distinguished Professor and Past President of the Santa Fe Institute (SFI), a non-profit research institute dedicated to the study of complex systems. Prior to joining the SFI in 2003, he was the leader and founder of the high energy physics group at Los Alamos National Lab.
About 20 years ago, West got interested in whether some of the techniques and principles from the world of physics could be applied to complex biological and social systems. In particular, he wondered if we could apply empirical, quantifiable and predictive scientific methods to help us better understand complex biological organisms and social organizations like cities and companies.
In the 1990s, his attention first turned to biology. There are enormous variations in the characteristics of living creatures, their live spans, pulse rates, metabolism, and so on. How do these characteristics change with body size? Why do human beings live roughly 80 to 100 years, while mice live only two to three years. Are there some common principles that apply to all living creatures regardless of size? Can we find empirical mathematical models that might allow scientists to ask big questions about life, aging and death?
Because the scaling is sublinear, that is, the exponent is less than 1, larger species are more efficient than smaller ones, needing less energy per pound. While an elephant is 10,000 times the size of a guinea pig, it needs only 1000 times as much energy. Furthermore, the bigger the organism, the longer it lives, and the longer it takes to grow and mature, all predicted by the same sublinear power law. This simple scaling applies to a large number of physiological variables besides metabolic rate, including how long the organism lives, how long it takes to mature, its growth rate and so on.
Along with SFI colleagues, West studied these scaling laws, and concluded that they were due to the internal structure that makes life possible, - the nutrient networks that have to reach every cell and capillary in a living organism. They modeled such networks, assuming that evolution would arrive at the most efficient structures possible, and came up with the ¾ power scaling between metabolic rate and mass that Max Kleiber had empirically observed in the 1930.
Their theory also explained why organisms grow rapidly when young but eventually stop growing. As the number of cells doubles, the number of capillaries rises by only 75 percent. As the organisms grow larger, the delivery system fails to keep up with the growth in cells, so eventually the growth must stop.
He then turned his attention from biology to social organizations. Could you view cities and companies as large-scale organisms, each with its own internal infrastructure connecting all its various components? Do similar scaling laws apply? “Is New York just actually, in some ways, a great big whale? And is Microsoft a great big elephant?, he asked in a fascinating video conversation, - Why Cities Keep Growing, Corporations and People Always Die, and Life Gets Faster. “Metaphorically we use biological terms, for example the DNA of the company or the ecology of the marketplace. But are those just metaphors or is there some serious substance that we can quantify with those?”
West and his SFI collaborators analyzed an extensive body of data about cities around the world to explore the scaling relations between population and a wide range of attributes, including energy consumption, economic activity, demographics, infrastructure, innovation, employment, and patterns of human behavior.
They discovered that the measurable infrastructure of cities, - e.g., the lengths of roadways and electrical lines, the number of gas stations, - scale sublinearly, just like in biological organisms, but with a scaling factor of .85. That means that if the population of a city doubles, its infrastructure needs to only increase by a factor of 1.85. This .85 scaling factor was true for cities of any size across the world as well as for any measurable infrastructure. Cities are real centers of sustainability, enjoying a 15% benefit in economies of scale.
The results were different for socioeconomic measures associated with the interactions of city residents. They also scale with populations, but instead of following a sublinear scale of .85, socioeconomic attributes scale at a superlinear factor of 1.15.
“That says that systematically, the bigger the city, the more wages you can expect, the more educational institutions in principle, more cultural events, more patents are produced, it's more innovative and so on,” explains West in the aforementioned video conversation. “Remarkably, all to the same degree. There was a universal exponent which turned out to be approximately 1.15 which translated to English says something like the following: If you double the size of a city from 50,000 to a hundred thousand, a million to two million, five million to ten million, it doesn't matter what, systematically, you get a roughly 15 percent increase in productivity, patents, the number of research institutions, wages and so on, and you get systematically a 15 percent saving in length of roads and general infrastructure.”
“However, some bad and ugly come with it. And the bad and ugly are things like a systematic increase in crime and various diseases, like AIDS, flu and so on. Interestingly enough, it scales all to the same 15 percent, if you double the size. Or put slightly differently, another way of saying it is, if you have a city of a million people and you broke it down into ten cities of a hundred thousand, you would require for that ten cities of a hundred thousand, 30 to 40 percent more roads, and 30 to 40 percent general infrastructure. And you would get a systematic decrease in wages and productivity and invention. Amazing. But you’d also get a decrease in crime, pollution and disease, systematically. So there are these trade-offs.”
West and his SFI colleagues then went on to explore another social organization - the firm. How come cities live forever, while companies do not? What’s the lifespan of a typical firm? Do scaling laws apply to companies? To find answers to these questions, they analyzed data about 30,000 publicly traded US companies from 1950 to 2009 across multiple industry sectors. Earlier this year, they published their findings in The Mortality of Companies.
The study found that scaling laws apply to companies as well. But unlike cities, which produce more per capita as they grow bigger, companies scale sublinearly, becoming somewhat less efficient as they get bigger. Revenue per employee and profits as a percentage of sales also decrease systematically, - with the exception of outliers like Apple.
Most surprisingly, their analysis led to a result that no one had predicted. Using a statistical technique called survival analysis, the SFI researchers discovered that “a firm’s mortality rate - its risk of dying in, say, the next year - had nothing to do with how long it had already been in business or what kinds of products it produce.” With the exception of outliers that’ve been around for a very long time, - DuPont was founded in 1802, Citi in 1812, GE in 1892, IBM in 1911, “the team estimated that the typical company lasts about ten years before it’s bought out, merges, or gets liquidated.”
The study showed that mergers and acquisitions are a more common reason for a company’s disappearance than outright liquidation. 45% of firms cease to exist because they’re either acquired or involved in a merger, so they persist in some form as part of some other entity. Rather that being a business failure, a merger or acquisition may actually make the resulting organization stronger and more productive.
Why does the typical firm live around 10 years irrespective of how well-established it is or what it actually does? The reasons for this finding are beyond the study’s scope, although the article hints that the competition for resources in biological ecosystems might provide some sort of insight. Perhaps companies can be seen as “competing for finite resources within a complex ecology of economic interactions… and their longevity is the result of their successes of learning and adaptation in these environments.”
“West sees his role as continually prodding others to look deeper, to apply more mathematical rigor, and to try to understand the big picture of life in scientific terms,” notes the s+b article in conclusion. “Otherwise, he says, ‘one is doomed to failure. Understanding is critical to mitigating problems, to innovating, to sustainability. You can argue that is why we are here. We are the universe’s way of knowing itself, we are that vehicle, and to play a role in that is wonderful.’”
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