Can the United States Win the Human Resource Race?

A large population also means a large talent pool. Even though the U.S. population is only one-quarter that of China, as shown in Figure 2.7, up until 2005, the number of U.S. college graduates still exceeded the number of Chinese graduates. College education is a basic requirement for research and development, and the number of college graduates reflects the size of human capital upon which innovation activities can draw. Throughout most of the 1900s, the United States has led the world in number of college graduates.

In recent years, with the expansion of college education, China has surpassed the United States in terms of number of college graduates. Many people doubt the quality of Chinese college education; it is often said, for example, that the quality of higher learning must be poor because China has not produced many Nobel Prize winners. However, the lack of Nobel Laureates is not unusual in the early stages of a newly emerging innovation powerhouse. Usually, top-quality academic research lags behind commercial innovation, and Nobel Prize awards, which usually reward scientists for innovative research that took place decades earlier, come much later. While the U.S. economy was the largest and most innovative in the 1900s, it was only after the Second World War that the United States became a leader in academic research and produced a significant number of Nobel Prize winners.

Table 2.1 shows the top five countries by number of Nobel Prize winners in science, including physics, chemistry, and economics. The Japanese economy became one of the leading innovators of the world in the 1980s, but not until after 2000 did it rank high in the league of Nobel Prize winners. We know that Japan's innovation has actually declined since the 1990s; the Nobel Prize actually reflects its scientific research capabilities in the 1980s—as I have pointed out, the prize tends to be awarded to winners some decades after their innovation is first pioneered.

In Table 2.1, the numbers in parentheses are the number of native-born scientists. This shows that in the United States and the United Kingdom, there are a large number of foreign-born Nobel Prize winners. In contrast, almost all Japanese winners are native-born scientists; this shows the strength of the United States and the United Kingdom in attracting foreign talent. I will revisit the relationship between immigration and innovation in later chapters.

In addition, academic research does not correlate perfectly with commercial innovation. The United Kingdom has a similar number of Nobel Prize winners as Germany, but in innovation (measured by number of patents) it has been much weaker. Korea thus far has had almost no Nobel Prize winners nor a world-class university, but still is able to foster innovative companies such as Samsung.

Over the next few decades, the college enrollment rate of many middle-income countries will increase to reach similar levels to that of developed countries; consequently, the number of college graduates will be a higher proportion of the total population in these countries. In China, the number of college students reached 33 million in 2012 compared with only 19 million in the United States. In India, the number of college students is expected to reach 26 million by 2020.

In the long run, India is likely to overtake China in having the largest number of college students. The low fertility rate in China will reduce the young cohort size significantly, and by 2040 the cohort size of 18- to 22-year-olds in China will be 40% smaller than that in India. By 2040, even if China's college enrollment reaches 60%, India will still have more college students than China, so long as India's enrollment rate exceeds 35%.

Of course, education quality also matters. Currently, U.S. universities are the best in the world. Moreover, although the United States has a smaller population, it has the advantage of attracting top talent from around the world. Consequently, the race for human capital among China, the United States, and India will be an interesting one to watch. I will discuss this in more detail in later chapters.

The Agglomeration of Talents

The distribution of high-tech companies is even more uneven than that of manufacturing companies. In the United States, a large number of innovative companies concentrate in Silicon Valley. Six of the top 10 high-tech companies—Google, Cisco, HP, Apple, Oracle, and Facebook—are all in Silicon Valley, and almost half the venture capital in the United States is invested in Silicon Valley. Silicon Valley, a small area from San Francisco to San Jose, has attracted the best talents from around the world, which in turn has led to the creation of some of the most valuable companies in the world.

In manufacturing, the agglomeration effect is a result of the proximity of related firms. The agglomeration effect in high-tech industries is a result of the proximity of creative talents. When creative people get together, the effect is like a chemical reaction. In the evenings in Silicon Valley, restaurants and coffee shops are full of engineers discussing the next technological breakthrough and startup opportunities.

With many high-tech firms being so close to each other, it is easy for workers to move between the firms. Inter-firm mobility is very high in Silicon Valley. If an engineer has a creative idea but is unable to secure funding or support from his company, he can easily take the idea to a different company, or he can start a new company with venture capital funding. High mobility not only enhances the exchange of ideas and innovations, but also allows startup firms to find talent quickly in order to scale their ideas or build on their initial success. Moreover, high mobility fosters a strong culture of entrepreneurship and joining entrepreneurial firms. Critically, high mobility also lowers the cost of failed entrepreneurship, because it is easy for people in a failed startup to find a new job. The necessary condition for high mobility is the agglomeration of many high-tech firms in one region.

Secondly, large cities not only provide for more efficient matching of talent with firms, but also allow more talents from diverse disciplines to work together. The recent innovations in Internet and software technology often require multidisciplinary cooperation, which further strengthens the advantage of large cities. Large cities are particularly attractive to highly educated young singles and couples, because, in the former case, it is easier to find a partner and in the latter case, because both spouses have a greater chance of finding good careers—despite the relatively high cost of living. In recent years, as the agglomeration effect increases, large cities across the world have grown larger and more expensive, while smaller towns, in particular, have witnessed a reduction in population and competitiveness.

The agglomeration advantage is a network effect of a large pool of innovative human resources multiplied by the size of the network. If one geographic region becomes the center of innovation for one industry, then the agglomeration effect tends to self-reinforce, and the center grows larger and more dominant over time. Outside Silicon Valley, cities like Boston also have top universities such as Harvard and MIT, but the dominance of Silicon Valley as the center for innovation has lasted for over 40 years, despite the very high cost of housing. Once a place becomes the center for innovation, the network effect kicks in, and it is very hard for other places to catch up. If we extrapolate from this logic, there should only be one center for each industry in each country. With globalization, there may even be only one center for each industry in the world.

Given that these big cities are more efficient and innovative, what prevents everybody from living in one city? Let's hypothesize: if the earth is no longer habitable, and we all move to a different planet, are we going to spread out evenly on this planet? Such a result would be highly unlikely. We are more likely to live closely together in one giant city. The current distribution of the world's population can be explained in historical terms; when economies were based on agricultural production, the population needed to spread out to do farming; during the Industrial Revolution, the population needed to be close to minerals and ports. Today, high-tech and service firms are the main drivers of economic activity; as a result, more people concentrate in large cities that are the centers of innovation.

A similar pattern is emerging in China too. Large cities in China have grown much faster than other cities. Housing prices in the top 30 cities have soared, while housing prices in smaller cities have stalled or fallen. The difference in housing prices between first-tier cities and small cities in China is now over tenfold. The worry is that there is a real-estate bubble in the first-tier cities. In Shanghai, Beijing, and Shenzhen, the housing price to income ratios are among the highest in the world. In general, people can move more freely within a country but not internationally, so there is a positive relationship between the population of a country and the population of its largest city. Given China's population, it is to be expected that its housing price to income ratio will be the highest in the world for the city centers of its top-tier cities.

Let's look at a graph to confirm these findings. Figure 2.8 shows the relationship between a country's population and the population of its largest city. The horizontal axis is the population size (in log scale) of the country, while the vertical axis is the population size of the largest city in that country. As shown on this graph, there is an obvious positive relationship between the population size of a country and the population size of its largest city. Not surprisingly, the more people in a country, the more of them live in the largest city.

With the exception of the Chinese cities, Asia's mega cities are generally above the regression line, which means that their populations are larger than their country populations predict. Tokyo, Jakarta, and Seoul are the largest metropolitan areas of the world. New York, Paris, and London are right on the regression line, which means that their sizes are just as predicted by their country population sizes. The largest city of Germany, Berlin, is much smaller than it should be. The reason is that Berlin's population did not grow from the 1940s to the 1990s, because it was geographically surrounded by East Germany during the Cold War. Since the reunification of Germany, Berlin, as the largest city in Germany, is growing rapidly, something that is a reverse of the general trend for the German population as a whole.

Noticeably, Shanghai is significantly below the regression line. If we stick to our analytical logic, Shanghai (and Beijing) should have much larger populations than they currently do. The largest city in the world is Tokyo, with 37 million residents; Seoul has 23 million residents and is still growing. China's population is 10 times larger than Japan's and 25 times larger than Korea's; however, Shanghai and Beijing, its largest cities (due to the past policy of restricting migration), have only 23 million people, which is too small to fully take advantage of the agglomeration effect. Therefore, Shanghai and Beijing are likely to grow significantly in the future, and real-estate prices in central Shanghai and Beijing will likely be amongst the highest in the world, rivaling those of Manhattan and Central London.

Recently, the city governments of both Beijing and Shanghai have been concerned about overpopulation; they have made efforts to turn away new migrants. However, limiting the size of large cities will not only hurt innovation, but is also unnecessary because congestion and environmental problems can be solved by way of good city planning, regulations, and innovation itself. Building high-speed rail connections between different parts of the city is one way to solve the congestion problems of mega cities. Silicon Valley is currently very congested, and one way to enable more people to live there is to build an efficient high-speed public transportation system such as the one in Tokyo. With over 37 million residents, Tokyo has a very efficient public transportation system. I will examine the topic of demographics and urban planning in more detail in Chapter 4.

Age and Cognitive Ability

In general, a person's physical ability peaks in their 20s. In a modern economy, however, what drives productivity is cognitive skill. How do cognitive skills change as one ages? Different cognitive skills age at different rates, as shown in Figure 2.9.

As shown in Figure 2.9, some cognitive skills hold up very well in old age. Verbal skills, for example, peak in the 50s and hold up very well until the 70s. Most cognitive skills, such as numerical skills, peak around the 30s and 40s, but fall significantly after the 60s. Perceptual speed, which reflects the ability to absorb new information, drops quickly after the 20s, which is consistent with the general finding that learning ability usually peaks at a young age.

In general, a person in his/her 30s or 40s is the most creative, because they have already accumulated the necessary academic and business knowledge, yet are still energetic, and can think and learn quickly.

An American economist, Benjamin Jones, analyzed the 300 greatest inventions over the last 100 years (Figure 2.10), and found that 72% of the great inventions were made by inventors aged between 30 and 49, and 42% of the great inventions were made by inventors in their 30s (Jones, 2005).

The age distribution of Nobel Laureates in science (physics, chemistry, medicine, and economics) over the last 100 years also showed similar results; most of the scientists made their revolutionary breakthrough in their 30s (Einstein discovered the theory of relativity at the age of 26). Of course, Nobel Laureates are not your average scientist or inventor. So let's look at the age distribution of patent inventors.

Figure 2.11 shows the age distribution of U.S. patent holders. As shown, patent productivity declines after the mid-40s. Particularly in the information technology field, the most productive and creative engineers and scientists are in their 30s and some even in their 20s.

Innovation and Entrepreneurship

It is not enough to have just engineers and scientists; entrepreneurs are critical to making disruptive inventions commercially successful. In economics, the topic of entrepreneurship is a difficult subject to analyze. Even the definition of entrepreneurship is elusive. One definition is firm formation, where an entrepreneur is anybody who registers for a new legal entity. By this definition, however, not all entrepreneurs are innovative. Owners of coffee shops can be called entrepreneurs, but they are just small business owners. Regardless of the definition, only innovative entrepreneurs are economically important. Innovative entrepreneurs may not be the sole owner of a startup, because the firm may have other financial investors like angel investors or venture capitalists, but what makes entrepreneurs different from researchers or professors is that they receive a big slice of the reward for their innovation.

Entrepreneurship is a critically important driver for innovation, especially disruptive innovations, which are, by definition, unpredictable. In contrast, micro innovation is usually a predictable improvement of existing technologies or processes. Large, established firms are good at micro innovations, whereas disruptive innovations usually come from new firms. For example, Amazon is an entrepreneurial firm that has disrupted the retail industry, and Uber is a new firm that has disrupted the taxi industry.

So why are incumbent firms not very good at disruptive innovation? The primary reason seems to be because incumbent firms tend to have a vested interest in the status quo (i.e. they benefit from the existing ecosystem and have little incentive to disrupt its functioning and risk their vested interest). For example, the executives at Barnes & Noble did not have the vision to create the best possible online book store. They created an online store only when they were forced to do so by competition from Amazon. Moreover, for disruptive innovation, the incumbent firms are slowed down by their bureaucracy. Lastly, the executives in large firms do not have the high power incentives of the entrepreneurs, who share a much bigger slice of the reward of success. Xerox invented many great technologies—such as the mouse, the local area network, and the graphical user interface—but the managers at Xerox did not have the incentive or flexibility to make risky investments to commercialize these great inventions. In contrast, entrepreneurs take more risks, have more decision power, and obtain a much bigger slice of the eventual reward.

Why is it important for innovative entrepreneurs to have a bigger slice of the reward? First, most innovators will work harder when there is a larger potential for reward. Moreover, only when the economic interest of the innovator is aligned with that of the business owner will the innovator be willing and able to take the right level of risk to make their innovation a commercial success. For micro innovations, where the outcome is reasonably predictable, large firms can design an effective bonus/reward system for innovation. But for disruptive innovation, which is inherently unpredictable, it will be impossible to design a bonus system that adequately rewards the innovator. The best bonus system is to offer a big slice of the profit, effectively making the innovator an entrepreneur. This is the reason why disruptive innovations can thrive only in a startup firm, but not in large incumbent firms, where the economic interest of the innovators is very different from that of the entrepreneurs.

Of the 10 largest high-tech companies, all but one company (the exception being IBM) were created over the last 40 years by young entrepreneurs before the age of 40; these firms quickly turned the latest technological inventions into a commercial success. After they grew very large, they invested heavily in research and development to retain a competitive advantage, which in turn sped up innovations in their industry.

Age and Entrepreneurship

I co-founded Ctrip.com at age 29 with three other co-founders, two of whom were aged 33 and the third aged 36. I have many entrepreneurial friends, and most of them were in their 30s when they started their companies. It seems like 40 is an inflection point: people are much less entrepreneurial in their 40s than they are in their 30s.

My anecdotal observation is borne out by the Global Entrepreneur Monitor, a study by Babson College and London Business School, which surveyed hundreds of thousands of entrepreneurs in many countries. The study found that 25–34 is the most productive entrepreneurial age range, and entrepreneurship declines rapidly after age 45 (Figure 2.12).

The age distribution of entrepreneurship can be explained by way of economic logic. Entrepreneurship is a long-term, high-risk investment. An entrepreneur typically sacrifices a lot of personal savings and leisure time for years, and the probability of failure is high. Young people are more willing to take on such risky investment, because they will have more time to enjoy the fruits of success. A 55-year-old is not going to start a company and work hard for 15 years, because at age 70 a successful entrepreneur will not have many years ahead to enjoy the success—if any. In addition, people after the age of 35 usually have more responsibilities, such as children and family, and therefore are less tolerant of financial risk. Furthermore, as explained earlier, most cognitive skills peak in the 30s and 40s, and learning ability is at its best in the 20s, therefore young people are better at learning new skills and adapting to new environments. Lastly, coming up with creative business ideas and running a startup firm requires certain industry knowledge and business skills, which are typically acquired through experience. Consequently, the best entrepreneurs are aged between 25 and 35, having accumulated some knowledge and experience, but still being open minded, adventurous, and risk-taking.

Entrepreneurship, Aging, and the Blocking Effect

Because talented entrepreneurs tend to typically be in their 30s, having a large number of highly educated 30-year-olds in the population is good for innovation, especially for disruptive innovation. Conversely, if a country is aging rapidly, there will be fewer potential young inventors and entrepreneurs.

There is also a blocking effect in an aging society, where older people block the vitality of younger people. The probability of being in a senior and influential position depends on the age makeup of the workforce. If a firm has an older workforce, it is less likely that young workers will be given much in the way of management responsibility, because the senior positions are already occupied by older workers. It is also for this reason that the age structure of a country is potentially an important determinant of entrepreneurship. A young society provides more opportunities for the young to acquire the skills necessary for entrepreneurship. Conversely, in an aging country, where the cohort size is shrinking over time, young workers are promoted more slowly, are less influential, have fewer skills, and hence are less capable as potential entrepreneurs. Therefore, in an aging country, not only are there fewer young people, but these young people are not as entrepreneurial because their development is blocked by a larger, older cohort.

The link between entrepreneurship and aging has been examined by economists at Stanford and Beijing University (Liang et al., 2014). I first noticed this link when I studied data on Japanese innovation. Overall, the innovation performance of Japan is adequate. Large firms in Japan invest heavily in research and development and acquire many patents. However, these inventions are mostly micro inventions, not disruptive inventions. For example, Japanese firms are continuously improving digital cameras, making them smaller, enhancing their quality, and adding more functionality; nonetheless, they have failed to produce disruptive inventions, such as the iPhone. Since the 1990s, despite churning out many patents, these large Japanese firms have been unable to keep up with the innovations of U.S. firms, especially new high-tech firms.

It was not always like this for Japanese firms. Japanese firms were very creative in the 1970s and 1980s, and came up with revolutionary inventions such as the Walkman, the digital camera, and the video game console. The Japanese economy grew spectacularly in these years, and many economists predicted that it would soon overtake the United States in terms of GDP per capita. However, after the bursting of its real-estate bubble in 1991, the Japanese economy stagnated for the next 20 years, while the U.S. economy pulled ahead, benefiting from a vigorous high-tech industry. The cause of Japan's lost decades is still being hotly debated. Some economists attribute the stagnation to the extended financial crisis, which was triggered by the real-estate bubble bursting. However, the problem with this explanation is that no other financial crisis in history has lasted for more than 10 years, whereas Japan's economy has stagnated for more than 25 years. Many economists today are realizing that the real culprit may well be the aging population and lack of entrepreneurship.

The reason that Japan's economy has underperformed compared with the United States is a lack of entrepreneurship and the failure to develop a dynamic IT industry, as the United States has done over the last 30 years. As shown in Table 2.2, five of the top 10 high-tech companies in the United States were founded after 1985; the founders were very young when they established their companies, with an average age of only 28. In contrast, none of the top 10 high-tech companies in Japan was founded over the last 40 years.

The new firm entry rate in Japan dropped from a 6–7% range in the 1960s and 1970s to 3% in the 1990s (Acht et al., 2004), which amounts to less than one-third of that in the United States and trails behind all other OECD countries (Karlin, 2013). According to an entrepreneurship survey (Global Entrepreneurship Monitor, 2012), the entrepreneurial propensity in Japan is the lowest among all the developed countries. In the United States, 4.9% of adults between the ages of 18 and 64 are working actively to establish new businesses, compared with only 1.9% in Japan.

Japan is the first country in the world to experience a rapidly aging population, as it did not have a baby boom period like the United States or Europe. Almost immediately after the Second World War, Japan's fertility rate fell rapidly and dropped below the replacement level in the 1970s. As a result, the Japanese workforce aged rapidly during the 1990s.

When the workforce becomes older, the promotion speed of young workers slows down. As shown in Table 2.3, in the 1970s about 32% of managers were under the age of 35. This ratio had dropped by almost half to only 16% by the mid-1990s.

In the 1970s, about one-quarter of department heads were under the age of 45, and that ratio dropped by two-thirds to only 8% in the 1990s. This is a direct result of the aging workforce, because typically promotions are based on seniority. When the age structure is a top-heavy reverse pyramid, young workers have to wait much longer to be promoted to managerial and executive positions, and they have less responsibility and accumulate fewer of the skills required to be entrepreneurs. When they finally do get promoted and acquire the necessary skills or financial and social capital to be entrepreneurs, they are already in their 40s or 50s and have passed the golden age of entrepreneurship.

Let's compare the entrepreneurial potential of young people in different countries using the Global Entrepreneurship Monitor survey data (Figure 2.13). Japan has the oldest demographic and also the lowest rate of entrepreneurship. Particularly striking is that, in most countries, 30 is the most entrepreneurial age; however, this is not the case in Japan, as 30-year-olds in Japan are even less entrepreneurial than 50-year-olds. The blocking effect magnifies the aging effect and damages the entrepreneurial vitality of the young disproportionately.

Over the last 30 years, not only has Japan failed to produce innovative new firms, but the existing large firms have also become very slow in creating disruptive innovations. The same blocking effect comes into play because an internal innovation project to create a breakthrough technology in a large firm resembles an “entrepreneurial project,” and is best led by a young leader in their 30s. However, a typical 30-year-old in a Japanese firm has a low-ranking position and fewer skills and political power in the company, and therefore cannot be an effective leader of such projects. So in an aging country (or aging firm), not only are there fewer creative new firms, but the existing large firms also tend to be more conservative and less creative.

Now, let's look beyond Japan and examine the relationship between aging and entrepreneurship in other developed countries. In Figure 2.14, the horizontal axis is the young ratio (i.e. the share of young workers [20–39 years old] in the total working-age population [to age 59]), while the vertical axis is the probability of the adult population starting a business. As shown in the figure, there is a positive correlation between entrepreneurship and the youthfulness of the population. Of these countries, Japan has the oldest population and the lowest entrepreneurial rate. In contrast, the populations in the United States and in Korea are younger and more entrepreneurial.

In Figure 2.14, if we compare the typical “youthful” country with 50% of the workforce under 40 to a relatively “older” country with just 45% of the workforce under the age of 40, we find that the entrepreneurial rate of the former is almost twice that of the latter. The effect is much larger than can be accounted for by simply having a larger number of young people, so the blocking effect (or other structural effects) must be at play to produce such a large difference in entrepreneurship rates.

If we look beyond the developed countries and include all the countries for which data is available, the positive relationship between a younger population and a higher level of entrepreneurship still holds (Figure 2.15). The age structure of China is currently still young, and Chinese workers are very entrepreneurial.

Finally, let's relate entrepreneurship to economic growth. Figure 2.16 shows the relationship between the GDP growth rate from 2000 to 2009 and the entrepreneurial rate. There is a clear positive relationship—higher levels of entrepreneurship correlate positively with higher economic growth.

See also Table 2.4, which relates the percentage of young people in a country's population to rates of entrepreneurship and GDP growth.