Stanford researcher Kenji Kushida says Japanese social norms are shifting from being highly unfavorable to a tech startup culture toward one much more supportive of it.

Japanese corporations are evolving and adopting a “startup culture” to boost their business creativity and country’s economic prospects, a Stanford expert says.

“We can see that over the past 15 years or so, changes to the overall Japanese political economic context as it undergoes gradual but substantive reform over the past couple decades have created a far more vibrant startup ecosystem in Japan than most people – both inside and outside Japan – realize,” said research associate Kenji Kushida of Stanford’s Walter H. Shorenstein Asia-Pacific Research Center.

Kushida wrote in a new research paper that, over the past decade, Japan has undertaken significant reforms that are now bearing fruit – reforms ranging from monetary and fiscal policy designed to encourage private investment to a range of regulations surrounding corporate law, university organization, labor mobility and financial market reforms.

As a result – and combined with changes and challenges facing Japan’s large company sector – the country’s people are embracing a “vibrant startup ecosystem,” Kushida said. He is optimistic that such a transformation can occur in a country where stability and corporate loyalty – not necessarily innovation or creativity – have long been dominant social and business values.

Now, large Japanese firms are adjusting to performance crises and uncertain futures. As a result, the Japanese people are learning that with economic opportunity – the kind that startups promise – there also comes the risk of failure.

“A generational shift is accompanying social normative changes that are becoming more supportive of entrepreneurship and high-growth startups. Entrepreneurs and high-growth startups are celebrated in the popular media and in major events more than ever before,” Kushida wrote.

Silicon Valley networking

The influence of California’s Silicon Valley is a factor. For instance, Japanese Prime Minister Shinzo Abe last year spoke at Stanford about how his country is learning the lessons of Silicon Valley and trying to build networks into the region. So Japan is likely to see an increase in the quality and quantity of high-growth startups, according to Kushida.

He said, “The current relationship between Japan and Silicon Valley is one in which Japanese firms, ranging from large firms to startups, are looking for ways to actively harness Silicon Valley. Large firms are trying by becoming investors in Silicon Valley venture capital firms, setting up their own venture capital arms, setting up branches in the valley, and trying to engage in ‘open’ innovation by entering into tie-ups and attempting to acquire select valley startups.”

A small but growing number of Japanese entrepreneurs visited Silicon Valley either to start their own companies or to grow firms that were started in Japan, Kushida said.

Still, Japan’s tech sector is a long way from what one finds in Silicon Valley, where many of the world’s most “disruptive” and game-changing firms are located. He wrote, “When compared to Silicon Valley, the ecosystem is still small in scale, but so is virtually every other startup ecosystem.”

A growing flow of Japanese entrepreneurs and CEOs is coming to Silicon Valley to get more of a sense of how things work, Kushida said, adding, “That is what we are helping through research at the StanfordSilicon Valley-New Japan Project as part of the Japan Program at the Shorenstein Asia-Pacific Research Center.”

Kushida said that if current estimates hold, Japan should expect successful startups, all supported by a “stronger ecosystem of startup-related players, combined with more open large firms.”

These large firms, he said, will spin off entrepreneurs who leave to launch other new companies, which will accelerate the startup cycle in Japan.

Spreading technology globally

Key challenges facing Japan’s startup culture, Kushida said, are the need for more entrepreneurial role models and the “overall lack of experience in creating followers.” On the latter, he explained that while Japan has excelled at producing tech products for use in its own markets, it would benefit by getting other firms and parts of the world to adopt its products and services.

“Think of the negotiations that Apple undertook with telecom carriers around the world to roll out the iPhone worldwide, or how Google is continually negotiating with governments such as those in the European Union to allow its services to be adopted broadly,” he said.

Other Stanford scholars, such as Takeo Hoshi, have recently written about the reasons Japan was not able to pull out of a long recession that resulted in virtually no growth in the 1990s. One problem, as Hoshi described it, was that the Japanese government was unable to introduce much-needed “structural reforms” to overhaul its economic structures to increase business competition – such as deregulation to cut operating costs for firms, a key attraction for startup-minded entrepreneurs.

Japan’s “lost decade” originally referred to the 1990s, though the country has still not regained the economic power it enjoyed in the 1970s and 1980s. Some say Japan has actually experienced two lost decades if the 2000s are counted as well.

Kushida’s paper, “Japan’s Startup Ecosystem: From Brave New World to Part of Syncretic New Japan,” was published in the Asia Research Policy journal.

Clifton Parker is a writer for the Stanford News Service.

The availability of accurate and reliable information on the location of impoverished zones is surprisingly lacking for much of the world. Applying machine learning to satellite images could identify impoverished regions in Africa.

One of the biggest challenges in providing relief to people living in poverty is locating them. The availability of accurate and reliable information on the location of impoverished zones is surprisingly lacking for much of the world, particularly on the African continent. Aid groups and other international organizations often fill in the gaps with door-to-door surveys, but these can be expensive and time-consuming to conduct.

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“We have a limited number of surveys conducted in scattered villages across the African continent, but otherwise we have very little local-level information on poverty,” said study coauthor Marshall Burke, an assistant professor of Earth system science at Stanford and a fellow at the Center on Food Security and the Environment. “At the same time, we collect all sorts of other data in these areas – like satellite imagery – constantly.”

The researchers sought to understand whether high-resolution satellite imagery – an unconventional but readily available data source – could inform estimates of where impoverished people live. The difficulty was that while standard machine learning approaches work best when they can access vast amounts of data, in this case there was little data on poverty to start with.

“There are few places in the world where we can tell the computer with certainty whether the people living there are rich or poor,” said study lead author Neal Jean, a doctoral student in computer science at Stanford’s School of Engineering. “This makes it hard to extract useful information from the huge amount of daytime satellite imagery that’s available.”

Because areas that are brighter at night are usually more developed, the solution involved combining high-resolution daytime imagery with images of the Earth at night. The researchers used the “nightlight” data to identify features in the higher-resolution daytime imagery that are correlated with economic development.

“Without being told what to look for, our machine learning algorithm learned to pick out of the imagery many things that are easily recognizable to humans – things like roads, urban areas and farmland,” said Jean. The researchers then used these features from the daytime imagery to predict village-level wealth, as measured in the available survey data.

They found that this method did a surprisingly good job predicting the distribution of poverty, outperforming existing approaches. These improved poverty maps could help aid organizations and policymakers distribute funds more efficiently and enact and evaluate policies more effectively.

“Our paper demonstrates the power of machine learning in this context,” said study co-author Stefano Ermon, assistant professor of computer science and a fellow by courtesy at the Stanford Woods Institute of the Environment. “And since it’s cheap and scalable – requiring only satellite images – it could be used to map poverty around the world in a very low-cost way.” 

Co-authors of the study, titled “Combining satellite imagery and machine learning to predict poverty,” include Michael Xie from Stanford's Department of Computer Science and David Lobell and W. Matthew Davis from Stanford's School of Earth, Energy and Environmental Sciences and the Center on Food Security and the Environment. For more information, visit the research group's website at: http://sustain.stanford.edu/

CONTACTS: 

Neal Jean, School of Engineering: nealjean@stanford.edu, (937) 286-6857

Marshall Burke, School of Earth, Energy and Environmental Sciences: mburke@stanford.edu, (650) 721-2203

Michelle Horton, Center on Food Security and the Environment: mjhorton@stanford.edu, (650) 498-4129