The challenges presented by increasing competition and the changing nature of work, and the opportunities presented by digital technologies, will require US companies and communities to strengthen their ability to innovate and create value. New prospects will be available to the people and organizations that can identify market opportunities and execute the business models and resources needed to commercialize solutions. Businesses will likely face increasing pressure to improve productivity and speed to market and will need to continue to reinvent their operations. Jobs along the value chain and the broader economy will be disrupted as advances in robotics and software enable machines to perform more complex tasks. In this environment, the best bet to aid the workforce that has been displaced by these changes is to advance their skills and ensure that the United States has an “innovative ecosystem” that can continuously attract and create jobs along the value chain and in all sectors of the economy.
This chapter presents five areas fundamental to an effective value creation ecosystem that will support US-based value chains: (1) widespread adoption of best business practices, (2) an innovative workforce, (3) local innovation networks, (4) flow of capital investments, and (5) infrastructure that enables value creation. It concludes with a discussion of federal programs that monitor the condition of various activities in US-based manufacturing and high-tech service value chains.
WIDESPREAD BEST BUSINESS PRACTICES
There are different forms of innovation, from improving processes to produce and deliver existing products to developing radically new products and services. Along this spectrum of innovation, different best practices are needed.
The techniques and approaches used to reduce lead times are quite sophisticated, and the people who know how to do it well have learned by applying the techniques for 25–30 years.
Best Practices for Improved Productivity, Quality, and Lead Time
There is a dramatic difference in performance between the best- and worst-managed companies in the United States. By improving their practices and management systems, underperforming firms can dramatically improve their productivity, quality of output, and lead time.
Larry Burns, former head of research at General Motors (GM), recounted his experience with GM’s supplier recognition program. He noted that many of the top suppliers remained the same from year to year, indicating that some companies are consistently, significantly better than most of their competitors.1 Research supports Burns’ observation. Even in narrowly defined industries in the United States—manufacturers of ready-mix concrete, for example, or of chewing gum—the differences in productivity between companies are enormous. An efficient producer can get twice as much output from the same inputs as another in the same industry during the same period (Syverson 2011). The clear implication is that significant industrywide gains in productivity are possible if the least efficient firms improve their efficiency. Closing the gap between the least and most efficient firms is not essential, however; reducing it by even 25 percent would greatly increase efficiency and growth—and value.
What accounts for productivity differences between companies? Research suggests that management practices based on lean manufacturing play an important role. Levels of management skill associated with these practices are strongly correlated with differences in productivity. This might seem an obvious conclusion, but until 10 or 15 years ago there were few systematic data to show that this is indeed the case. Now such data are starting to be collected. For instance, the US Census Bureau has added a management survey to its annual survey of manufacturers, which supplies data on more than 30,000 manufacturing establishments in the United States. The first year of these data showed that 27 percent of the companies had adopted less than half of a group of management practices involving performance monitoring, targets, and incentives. At the other end of the spectrum, just 18 percent of companies had adopted at least 75 percent of those practices (Bloom et al. 2013). Since these practices are central to lean manufacturing (Womack et al. 1990), the extent to which
1 Comments by Larry Burns at meeting of the Foundational Committee on Best Practices for the Making Value for America Study, September 23, 2013, the National Academies, Washington, DC.
a company follows them offers a good measure of its commitment to the lean production system.
Similarly, the World Management Survey (WMS) has been gathering data since 2004 on the same management practices from more than 10,000 companies in 21 countries. The data show that well-managed companies “massively outperform” competitors that are not well managed: “They make more money, grow faster, have far higher stock market values, and survive for longer” (Bloom et al. 2011).
According to the WMS survey, US firms are, on average, the best managed in the world, although they are not much ahead of companies in Japan, Germany, and Sweden. The countries with the lowest scores were Brazil, India, China, and Greece (Bloom and Van Reenen 2010). But the gap between the bottom and top performers in any country trumps the differences between countries. Several companies in India and Brazil perform just as well as the top performers in the United States, and the bottom 15 percent of companies in the United States rank below the average for firms in China or India, two countries that have the most poorly managed firms, on average, among the nations surveyed.
For US companies to maintain—or increase—their competitive advantage, best practices will have to be widely adopted, especially at the most poorly managed companies. This does not presume that all businesses will apply these management practices exactly the same way, but companies of all sizes and in all industries deal with many of the same problems, such as redundancy of work and the need for continuous improvement, that can be addressed by implementing these practices. Since these practices improve a company’s performance, it may seem that it would be easy to encourage companies to adopt them, but in reality it is very challenging.
Toyota’s James Bonini, who helps other companies apply the Japanese automaker’s lean production system to their own production efforts, articulated some of the challenges. First, he said, is blindness about how much operations can improve. Many companies say they need to reduce their costs by 10 percent or they will have to move to another country or shut down. They are convinced that it is impossible to reduce costs this much while operating in the United States. After working with these companies to implement lean manufacturing, Bonini has found that they can reduce costs by as much as 40 or 50 percent.
Second, the system must be learned by doing. The techniques and approaches used to reduce lead times, for example, are quite sophisticated, and the people who know how to do it well have learned by applying these techniques for 25–30 years and thus have developed very high levels of expertise in achieving the seemingly basic principle of “just in time.” When Toyota teaches its methods to another company, it focuses on a particular area in which lean production will be implemented. It instructs the people in the company on the conceptual underpinnings of lean production—the philosophy, technical
tools, role of management, and so on—but it also picks two or three company employees to work side by side with the people from Toyota in the implementation. Working with the more experienced Toyota representatives, the company workers learn how to put the principles into practice, and can then share their understanding with other company employees. This process of learning by doing in a company can take as much as two years.2
Often, best practices are transferred to new companies through partnerships. GM first learned how to implement the Toyota Production System through a joint venture with the New United Motor Manufacturing, Inc. (NUMMI) plant in Fremont, California. Through this cooperative venture, GM managers learned the principles and practices of the system first-hand and after expending considerable effort spread the knowledge throughout the company (Inkpen 2005). The same was true for GE when it outsourced the production of its appliances to LG and Samsung; Kevin Nolan of GE reported to the committee that the company was able to see firsthand how LG and Samsung operated through these partnerships. These companies have embraced lean production, he explained, and have been doing it for a very long time. “In the US, everyone talks about lean manufacturing but very few people actually know how to do it,” he said. Once GE workers were familiar with the lean operations at these companies, they understood how to implement them at GE.
Best Practices for New Product and Service Innovation
Developing new solutions requires the ability to identify market opportunities, the creative skills to formulate new business models, and the acumen to implement the resources needed to create and deliver new products and services that customers value. Many experts have proposed best practices for innovation and new product and service delivery (Kelley 2001; Carlson et al. 2006; Brown 2009; Christensen and Raynor 2003). Several of these practices emphasize the need for businesses to improve their abilities to both (1) understand customers and identify their needs and desires, and (2) repeatedly innovate in distinctive ways to gain a competitive advantage. These two capabilities are becoming increasingly important as competition intensifies and the pace of technological change increases.
Larry Burns spoke with the committee about the necessity of these capabilities: “To succeed in today’s hyper-competitive world, companies must explicitly design for positive experiences with their products and services, and constantly innovate around every facet of these customer experiences.” An iterative process of understanding customer experiences, building and trying out
2 Remarks of James Bonini at “Making Value for America: A National Conference on Value Creation and Opportunity in the United States,” February 27, 2014, Beckman Center of the National Academies, Irvine, Calif. See also Manyika et al. (2012).
a prototype, improving the solution, and applying lessons learned to the next innovation are all critical to maintaining a competitive advantage. Companies that follow these principles—such as Apple, Google, and IBM—are considered among the most valuable brands by consumer and financial rankings, Burns observed.3
The concept of design thinking exemplifies how businesses can gain insights about their customers and explicitly design for positive customer experiences with their products and services (Brown 2009; Martin 2009). A number of businesses have implemented this approach. Kaiser Permanente, with the help of the consulting firm IDEO, used design thinking to improve its customers’ ability to get medical treatment (Brown 2008). IDEO’s study of Kaiser’s patients revealed that they often became annoyed long before they saw a doctor because of poor experiences checking in and waiting in the receiving room before they were able to meet with a medical professional, especially during nurse shift changes. The study found that wait times were particularly long because nurses routinely spent the first 45 minutes of their shift debriefing the departing shift about the status of patients. IDEO developed simple software and new procedures so nurses could input data throughout a shift and call up previous shift-change notes. As a result, the time between a nurse’s arrival and first interaction with the patient was cut in half.
Additional sets of best practices emphasize how businesses can innovate in various ways to differentiate their offerings from competitors and give them a competitive advantage. For example, the innovation strategy firm Doblin has identified “Ten Types of Innovation” that businesses have used to gain competitive advantage. The accompanying case studies suggest that companies improve their advantage by combining different modes of innovation along the value chain—in product performance, services, method of delivering offerings to customers, and other areas (Keeley et al. 2013).
Empirical research has provided some support for a number of the principles promoted by the approaches described above. Many studies have tested the driving forces of successful product or process innovation by analyzing the characteristics of large businesses and their propensity for innovation. While these studies have conflicting results regarding the influence of some factors, they largely agree on the importance of differentiation, monitoring of customer needs, and company culture (Becheikh et al. 2006). Companies that adopt a differentiation strategy—developing products that meet customer needs in unique ways and are difficult to replicate—tend to innovate intensively and achieve a greater competitive advantage. Monitoring customer behavior to understand the evolution of buyers’ needs and desires has been shown to be beneficial for innovation. And businesses that have a CEO who sets challenging goals,
3 Remarks of Larry Burns at meeting of the Foundational Committee on Best Practices for the Making Value for America Study, September 23, 2013, the National Academies, Washington, DC.
employees who are empowered to take on new projects, and a structure that encourages interaction between functional units—in other words, a company culture of innovation—tend to deliver more innovative products and services.
With respect to monitoring customer needs, several methods center on “customer-led” practices, involving customers directly in business decisions and responding quickly to their feedback. These practices may entail customer focus groups, customer interviews, or ethnographic studies of customers using a product. For example, the software company Intuit assembled a 6,000-per-son “inner circle” of customers to serve as a standing focus group (Allen et al. 2005). A company with even greater customer involvement is Quirky, which solicits ideas from inventors and posts them to its online community for feedback. The ideas with the most positive feedback get turned into prototypes, which are then further reviewed by users, who make suggestions for improvements, packaging, and marketing and also play a role in setting the price (Economist 2012). The lean startup method applies similar customer-led principles to business startups, to learn from customers and get feedback from the market (Ries 2011).
Exactly which set of best practices is best suited for a particular firm will depend on various factors: whether the company is new or established, its size, location, industry, and so on. Best practices can be identified for widespread use by further investigating businesses with excellent innovation performance across a wide variety of industries and contexts and determining the practices they have in common.
Spreading Best Practices
An important way to improve the US capacity to create value will be to spread recognized best practices to as many companies as possible, making the best a little better and the worst a lot better.
Partnerships are a well-known means of spreading operational best practices such as lean production, perhaps best exemplified by the GM–Toyota NUMMI plant. Several government programs have also developed to help facilitate the transfer of best practices across businesses and industry sectors. For example, the Manufacturing Extension Partnership (MEP) administered by the Department of Commerce collaborates with manufacturers to help them adopt lean production, formulate export plans, and reduce energy use, among other services. These partnerships have successfully spread lean production best practices and are generally considered worthy of investment (NRC 2013c); according to the partnership’s website, “since 1988, MEP has worked with nearly 80,000 manufacturers, leading to $88 billion in sales and $14 billion in cost savings, and it has helped create more than 729,000 jobs.”4
Could comparable partnerships be used to spread best practices for identifying market opportunities and commercializing solutions? A number of biotech and pharmaceutical companies have recently established research partnerships to analyze open databases of clinical and genetic information, with the aim of collaboratively developing new drugs and diagnostics (Lund et al. 2013a). These partnerships benefit the participating companies by expanding involvement in problem solving and testing and reducing licensing and transaction costs when firms can access knowledge produced by the collaborative network (Battelle 2012; David et al. 2010). Experience with similar partnerships shows that networks with private sector leadership and funding are more likely to be associated with higher business outcomes (Kingsley and Klein 1998). Other important considerations are the compatibility of participating businesses in terms of creating a cooperative environment and achieving a critical mass of individuals who are both sufficiently knowledgeable and empowered to make decisions on behalf of their companies (Welch et al. 1997; Huggins 2001).
AN INNOVATIVE WORKFORCE
At the heart of innovation are the innovators themselves—the people who generate new ideas for creating value and who, with help, turn those ideas into reality. The first step in encouraging innovation, therefore, is to ensure a steady supply of innovators.
The most basic incubator of such talent is the education system, which should develop students’ skills in science, technology, engineering, and mathematics (STEM) and real-world problem solving from kindergarten through college, continued education, and training in the workplace. Improving the participation of women and people of diverse races and socioeconomic backgrounds in STEM education and hiring is also important to create innovative teams in businesses.
There is a need to give more students access to hands-on experiences designing and making, and to nurture the urge to innovate.
Education and Training
Critical Skills and Experience
Innovation requires scientists, engineers, technicians, operators, managers, analysts, and many others with the skills to conceive of an innovation and then develop it from idea to reality. Perhaps the most fundamental skills for
innovation along the value chain are those in the STEM disciplines, from software engineering to tooling operations and from molecular biology to social psychology.
US competitiveness depends on improving STEM education and increasing the number of students who pursue it (NRC 2011). By many accounts, the US system of higher education remains the best in the world. However, a number of concerns persist about US STEM education, particularly K–12 science and math education and the quantity of science and engineering college graduates, and these concerns negatively affect the perception of the country as an attractive place to locate activities along the value chain. (The Appendix discusses these concerns in detail.)
Critical thinking and creativity are as important as technical skills. It is not enough to learn facts and procedures by rote; students need to learn to evaluate a situation by asking questions, observing, collecting further information, and subjecting the collected data to a thoughtful analysis to identify mistakes and weaknesses and come up with alternative possibilities. Creative critical thinkers constantly probe and evolve their own interpretations and ideas.
It is therefore important that schools and other educational programs nurture the urge to innovate. Experience in various STEM programs around the country has demonstrated that opportunities for students to innovate solutions to real-world problems can be an effective way of teaching principles of engineering, science, and mathematics (NRC 2011), the fundamentals of innovation in a hands-on way, and the role of innovation in improving the world around them.
Students are gaining experiences developing real-world solutions through a variety of formal learning and extracurricular programs, but these opportunities are not yet widespread. The Next Generation Science Standards (NGSS) identify engineering design content and practices that all K–12 students should learn (NRC 2013a). The supporting framework for these standards had been adopted by eleven states as of August 2014. In addition, “Maker Spaces,” community spaces with the parts and equipment necessary to build mechanical and electronic devices, and programs such as FIRST (For Inspiration and Recognition of Science and Technology), which offers design and build competitions for K–12 students, offer students opportunities to create and make real-world products. But many schools and communities do not yet have similar opportunities in place. More students need access to hands-on experiences designing and making things (NRC 2013a).
Some colleges and universities are providing their students with opportunities for such experiences. UC Davis started the Engineering Student Startup Center (ESSC) and the Engineering Fabrication Laboratory (EFL) to provide all undergraduates and graduates with the resources to develop and prototype new ideas and experience what it is like to be an entrepreneur. The extensive ESSC and EFL facilities include a machine shop and a rapid prototyping
machine that allows students to 3-D print their designs.5 Stanford University also has a number of programs to encourage design and real-world experience. For example, Stanford Biodesign involves students and faculty from over 40 departments and provides innovation classes, mentoring, fellowships, and career services.6
Access to higher education and training is especially important for lower-skilled workers, who are most affected by technological developments and changing business models.
As the nature of work changes across the value chain, access to higher education and training is especially important for lower-skilled workers, who are most affected by technological developments and changing business models.
Unfortunately, this part of the workforce also faces greater barriers to higher education. The rising costs of college attendance put greater strain on low-income families, and students from these families lack the social supports to help them complete degree programs (Haskins et al. 2009). Only 30 percent of college students from families in the lowest quartile of the income distribution complete their degrees, less than half the completion rate of the average student (Holzer and Dunlop 2013).
Higher education organizations are experimenting with new models to reduce costs and improve access. One basic approach toward accomplishing this goal is to track the cost-effectiveness of university and college programs. Measuring the productivity of these programs, taking account of outcomes and costs, is seen as the most promising strategy for improving the affordability of a quality higher education (NRC 2012b, p. 1). Metrics that show the productivity of university and college programs are needed to enable students to make informed decisions about the value of enrolling in a particular program, and to support decisions about ways to improve cost-effectiveness.
Organizations are also reducing the costs of higher education and improving access by creating more flexible pathways to enter and exit degree programs, particularly with community colleges, which offer low-cost pathways to transfer to bachelor’s degree programs. Almost one half of Americans with bachelor’s degrees in science or engineering attended a community college at some point
5 Information is available at http://engineering.ucdavis.edu/undergraduate/engineering-studentstartup-center/ (accessed February 5, 2015).
(Tsapogas 2004). Unfortunately, students, especially from low-income families, often face barriers that prevent them from successfully transferring from community colleges to university programs. These include a lack of advising services to help them choose appropriate courses, insufficient information about the transfer process and financial aid options, and a lack of alignment of curricula content at community colleges with university programs (ACSFA 2008). Several state and national initiatives have sought to reduce these barriers; for example, the National Articulation and Transfer Network and the Kentucky Council on Postsecondary Education have implemented programs to help institutions better align their course requirements, provide students and advisors with more information about transfer guidelines, and offer mentoring services to support the transfer process (ACSFA 2008).
In addition to transfer programs, organizations are establishing methods to recognize knowledge and skills gained without a completed degree (Lund et al. 2013b). The Manufacturing Institute, for example, has developed the Manufacturing Skills Standard Certification System, which recognizes specific production skills applicable to all manufacturing industries. Nationally recognized certification systems also exist in energy and information technology fields. Such certifications allow students without a bachelor’s degree to gain higher-paying jobs—on average, workers with certificates earn 20 percent more than high school graduates do (Carnevale et al. 2012). Moreover, some of these credentials are “stackable,” meaning that they are part of a series that can be accumulated over time and count toward a degree-granting program. This structure is particularly important for lower-income students and dislocated workers, who often have family and work responsibilities that prevent them from completing a continuous degree program (Ganzglass 2014). Widespread recognition of these types of certifications by degree-granting programs and employers has the potential to significantly improve employment and career outcomes (Ganzglass et al. 2011).
Preparing the workforce with the education and skills needed to succeed in the face of changing technologies and business models requires shared responsibility among educators and employers—and both parties can share the benefits.
Some colleges are experimenting with online education and computer-based tools to reduce costs and boost retention. These tools enable personalized learning and rapid feedback that improve student learning. A study at Carnegie Mellon University found that college students studying statistics through an online environment, supplemented with weekly face-to-face meet-
ings with an instructor, learned a full semester’s worth of content in half the time of equivalent classroom instruction (Lovett et al. 2008). Online tools can also reduce education costs (Bakia et al. 2012): once online course materials are developed, they can be reused at a relatively low cost and distributed to large numbers of students. Further cost reductions can be achieved by redesigning courses to allow for more effective use of an instructor’s time and transferring some activities to computers.
Preparing the workforce with the education and skills needed to succeed in the face of changing technologies and business models requires shared responsibility among educators and employers—and both parties can share the benefits. Recent partnerships between schools and employers combine classroom-based learning with work experiences, many of which target students who are at higher risk of dropping out of high school and put them on a track to a college degree and a skilled job. For example, several schools in Chicago and New York follow the Pathways in Technology Early College High School (P-TECH) model, in which employers partner with high schools and community colleges to design a curriculum that meets state learning standards and leads students to higher degrees and entry-level jobs in areas such as computer science, biotechnology, electromechanical engineering technology, and robotics. One particularly valuable aspect of P-TECH schools is that they can help disadvantaged students—often members of underrepresented minorities—transition to college and to a well-paying job and career (Dossani 2014). Students attend high school for six years, with the chance to earn an associate’s degree along the way. They are paired with mentors from the school’s corporate sponsor and given opportunities to participate in summer internships and job shadowing. Graduates gain skills that are attractive to the corporate partner and are given priority consideration for jobs at these companies (see, for example, Foroohar 2014).
Chicago’s Austin Polytechnical Academy is another example of an employer-education partnership that leads lower-income students to higher degrees and skilled jobs. The school provides an advanced manufacturing curriculum that was jointly developed with local manufacturers, providing instruction in manufacturing, design, engineering, and business skills such as networking, in addition to standard courses in math, science, English, and social studies. The partnership with employers enables students to participate in internships and job shadowing experiences over the summer and be mentored by experienced professionals, all of which can give them the skills and social supports necessary for a college degree and career.
Nationwide, there are over 660 schools like this in 36 states, Washington, DC, and the US Virgin Islands7 and they are showing very promising results.
Their students are substantially more likely to complete high school—90 percent receive a diploma, compared to a national average of 78 percent—and are better prepared than their counterparts to earn a higher degree (Webb and Gerwin 2014).
Governments at all levels share responsibility for providing access to high-quality education. But the committee is concerned that local, state, and federal investments in education are not adequate to ensure that all American students have access to an effective, rigorous education. In fact, according to a study from the Center on Budget and Policy Priorities, at least 34 states provided less funding per student in the 2013–14 school year than in 2007–08 (Leachman and Mai 2014), and 13 of those states cut student spending by more than 10 percent during that period.
If the United States is to retain international strength and leadership in value creation, committed support for the education of all its current and future workers must be a priority.
Employer Training Programs
In addition to partnerships with high schools and higher education institutions, employer training for both lower-skilled and professional employees is an important component of advancing workforce skills. Employer training programs raise the earnings potential of low-skilled as well as professional workers and can substantially increase productivity, benefiting both employer and employees (Bartel 1994; Veum 1995; Ichniowski et al. 1995; Krueger and Rouse 1998; Hansson 2007). A review of employers’ return on investment from training programs indicates that returns may be much larger than previously believed, in some cases as high as 100–200 percent (Bartel 2000). For example, a team-building training program provided by Garrett Engine to randomly assigned maintenance teams led to faster maintenance response and completion times by the teams that received the training, reducing total downtime by 14 percent. The company calculated the return on investment of the training at 125 percent (Pine and Tingley 1993).
Despite the sizable returns employers can receive from training programs, both employers and employees report that the current level of employee training, especially in small businesses, is not adequate (Lynch 2004). Small businesses in particular face a number of barriers that prevent them from delivering adequate training programs (Lynch 2004; Panagiotakopoulos 2011; Dutta et al. 2012). The costs of training programs per employee are higher in smaller businesses because they cannot spread fixed costs over a large group of employees. Turnover rates are often higher, discouraging employers from investing in the skills of workers lest they leave the company. And small businesses struggle more with the time and short-term productivity losses required for employees to receive training.
Policies and partnerships can address many of the barriers to training. Government-provided training subsidies for employers are one option (Lynch 2004), as are partnerships or mediating organizations that coordinate between employers, labor, and government to provide workforce training. These partnerships entail co-investment by employers, employees, and government; the training curriculum is jointly determined by these three sets of stakeholders; and the skills learned in training are certified to ensure uniform quality standards and portability between employers (Lynch 2004). Examples of these types of partnership training programs geared toward particular sectors—information technology in New York and manufacturing and health care in Milwaukee—have improved job outcomes for employees and low-income adults struggling in the labor market, with almost a 30 percent increase in earnings attributed to the training (Greenstone and Looney 2011).
The Importance of Teams for Innovation
Despite the popular image of a lone inventor, successful innovation is almost always the result of teams working together on a problem. Innovation requires talented people all along the value chain: engineers, scientists, and business leaders who develop inventions and create jobs; tooling engineers and others who create processes to produce goods faster and more efficiently; marketing and business analysts who gain insights on customer needs and market opportunities; and technical support and retail personnel who deliver positive customer experiences.
Individuals and individualism do play an important role in innovation, particularly in the discovery or inventive stage (Černe et al. 2013; Ramamoorthy et al. 2005). Indeed, nations with more individualistic cultures tend to have more patents and highly cited scientific publications (Taylor and Wilson 2012; Gorodnichenko and Roland 2011; Shane 1993). However, effective teamwork is necessary for successful innovation, and collectivism and collaboration are linked to higher rates of commercialized innovations (Černe et al. 2013; Tiessen 1997).
Thomas Malone, a professor in the Sloan School of Management at the Massachusetts Institute of Technology (MIT) and director of the MIT Center for Collective Intelligence, suggested that in the United States there is a “cultural illusion” about the importance of individuals. There are certainly occasions when individuals make a big difference, but that happens much less often than most people think, he said. In general, success in innovation and other
business areas is due to groups of people and how well they work together rather than to the contributions of one or a few singular individuals.8
Even when individuals set aside their personal goals and work as a team, putting together an effective team is more complicated than simply assembling competent individuals. There has been a great deal of research into what makes an effective team, but there is still much that remains unknown. Malone and his colleagues have demonstrated a group intelligence that is analogous to the IQ of individuals (Woolley et al. 2010): it is not a function of the intelligence of the individual members but rather of the way they interact. In other words, putting the smartest people in a group will not necessarily result in the smartest team.
Experiments conducted by researchers at MIT and Carnegie Mellon University have shown that the average intelligence of group members and the highest intelligence level of any individual in the group are not very good predictors of a group’s performance (Woolley et al. 2010). Rather, the research shows that a team’s performance is strongly associated with the social and cognitive characteristics of its members: their ability to interpret each other’s emotions and to speak in turns rather than dominating discussions is strongly correlated with the group’s effective performance on a large variety of tasks such as brainstorming, solving puzzles, building objects with a complicated set of constraints, making moral judgments, and negotiating over limited resources. Cognitive styles are also an important aspect of group intelligence, which increases as cognitive diversity increases—but only to a point; if a group becomes too cognitively diverse, the collective intelligence tends to drop (Aggarwal et al. 2013).
Multiple studies on collective intelligence have found that group performance is strongly associated with the percentage of women, leveling off at about 75–80 percent of the group (Woolley and Malone 2011; Aggarwal et al. 2013). Women tend to score higher on tests of social perceptiveness—the ability to read team members’ emotions in their facial expressions and the ability to listen to others. Since these abilities are important factors of collective intelligence, the number of women in a group is a good indicator, on average, of the team’s performance. This finding has intriguing implications for the makeup of teams in businesses, and is especially important for manufacturing, high-tech services, and entrepreneurship groups of all types, where women are substantially underrepresented (Khanna 2013; Klobuchar 2013; Mitchell 2011).
8 Remarks of Thomas Malone at “Making Value for America: A National Conference on Value Creation and Opportunity in the United States,” February 27, 2014, Beckman Center of the National Academies, Irvine, Calif.
Any effort to increase the nation’s ability to create value should have, as one of its core principles, a commitment to making sure that all individuals have an equal opportunity to take part in that effort.
Beyond the number of women on a team, it is important to take the overall diversity of a team into account, for both practical and ethical reasons. The practical reason is that greater diversity of thought generally leads to more and better innovation: The more perspectives and life experiences and ways of thinking a team brings to a problem, the more ideas are likely to be generated. Furthermore, teams that include people of different gender, race, cultural or socioeconomic background, sexual orientation, and other characteristics are more likely to produce solutions that will appeal to a broad array of customers.
There is widespread belief among business executives that diversity among employees and managers is a competitive advantage for their company and that diversity is actually a key factor in successful innovation. A survey of more than 300 senior executives worldwide found that 85 percent of them believed that a diverse workforce, offering different perspectives, leads to greater innovativeness (Forbes 2011). A growing body of evidence supports the notion that diversity of demographic characteristics, thought, and culture is important for team performance and overall business outcomes (Hong and Page 2004; Hoogendoorn et al. 2013; Johansson 2004; Barta et al. 2012). One study analyzed data from the National Organizations Survey, which samples for-profit businesses across the United States, and found that racially diverse businesses had, on average, greater sales revenue, more customers, and greater market share than businesses that were not racially diverse; the same was true of businesses with a relatively even mix of male and female employees compared to those that were less gender diverse (Herring 2009).
Research has shown that cultural diversity also is linked to innovation performance and economic growth. Regions with more cultural diversity, in terms of the share of their foreign-born population, tend to have higher productivity, R&D output, and entrepreneurship (Niebuhr 2010; Ottaviano and Peri 2006).9 In fact, over 25 percent of engineering and technology companies in the United States had at least one foreign-born member on their founding team (Wadhwa et al. 2007). The benefits of cultural diversity are also evident in patenting rates (Parrotta et al. 2012; Chellaraj et al. 2005): In 2011, 76 percent of patents from
9 As one might expect, the benefits of cultural diversity are strongest when all team members are fluent in the same language; otherwise, the positive effects of cultural diversity on innovation are counteracted by communication difficulties (Parrotta et al. 2012).
the top ten patent-granting universities had at least one foreign-born inventor (PNAE 2012). By some estimates, increasing the share of immigrant college graduates by 1 percent increases the per capita patenting rate by as much as 18 percent (Hunt and Gauthier-Loiselle 2010).
The ability to attract talented students and workers from diverse cultures around the world has historically been a great strength of the United States. Since 2000 foreign students with temporary visas have earned 39–48 percent of US doctoral degrees in the natural sciences and engineering (NSB 2012). In the past, a large percentage of these foreign nationals remained in the United States after graduation and started new businesses or otherwise contributed to the economy. But there is evidence that many are now choosing to return to their home countries. It also appears that students from the top programs are somewhat more likely to choose their home countries over staying in the United States compared to students from less high-ranking programs (NSB 2012). The loss of this cultural diversity, especially from such highly trained graduates, is not favorable for the US economy.
Apart from the practical benefits of diversity, there is also a clear ethical argument to be made for diversity. Creating value is not an end in itself. It improves life by making it possible for people to have more of the things they need and want. A variety of historical, social, and psychological barriers, including innate biases, have prevented many underrepresented groups from gaining equal access to value creation opportunities (Steele 2010; Kahneman 2011; Banaji and Greenwald 2013). If this portion of the population is deprived of the opportunity to take part in the creation of value, they will also be denied the opportunity to partake of many of its benefits. The people most intimately connected with the creation of value are also those who tend to have the highest-paying jobs and thus the greatest ability to enjoy the benefits of innovation and a growing economy. Moreover, they enjoy not only the financial benefits of value creation but also the psychological benefits of knowing they are contributing value to their fellow human beings. Conversely, individuals who are left out of value creation find themselves not only at the bottom of the socioeconomic ladder but also deprived of the chance to contribute in this important way.
A country that is truly successful in making value will leave none of its citizens behind. Thus any effort to increase the nation’s ability to create value should have, as one of its core principles, a commitment to making sure that all individuals have an equal opportunity to take part in that effort.
Some companies have recognized the importance of diversity and have implemented programs that have successfully increased recruitment and retention of women and underrepresented minorities. Deloitte & Touche, for example, instituted an effort to track the progress of women in the company, ensure transparency of mentorship and promotions, and promote better work-life balance for all employees (Harrington and Ladge 2009). Through this initiative, the company was able to close the gap in turnover between women and men
and achieve a higher number of women in top positions than at any of its competitors. In the early 1990s Xerox established a goal of becoming the employer of choice for women and minorities. The company created an internship program for women and minorities and revised its hiring and promotion practices to create more lateral promotion opportunities and publicize the criteria for these promotions to all employees (NRC 1994). By 2010, 50 percent of managers at Xerox were women and 23 percent were minorities, up from 23 percent and 19 percent, respectively, in 1991 (Butterfield 1991; Xerox 2013). The percentage of all employees that were women rose from 32 percent to 52 percent over that period and minorities increased from 26 percent to 39 percent.
In addition to corporate initiatives, several universities have implemented successful efforts to improve the recruitment and retention of women and underrepresented minorities. The University of California, Berkeley redesigned its introductory computer science courses and eliminated aspects that studies showed deterred women. It reoriented the courses to emphasize the relevance of computing to real-world problems, beginning each class with a discussion of a recent tech-related news article, and added team exercises. Enrollment of women in introductory computer science classes significantly increased as a result, reaching just over 50 percent—the highest percent in the history of Berkeley’s digital records. Although the overall share of female computer science majors at UC Berkeley and Stanford is still only 21 percent, the shift in introductory computer science classes is a good first step in the right direction (Brown 2014). Other exemplary higher education programs include the University of Michigan Women in Science and Engineering Residence Program (WISE-RP), the multi-university Gateway Engineering Education Coalition, and the Meyerhoff Scholars Program at the University of Maryland, Baltimore County (UMBC). All of these programs demonstrate eight characteristics that contribute to their success: (1) institutional leadership, (2) targeted recruitment, (3) engaged faculty, (4) personal attention (such as mentoring for individual students), (5) peer support, (6) enriched research opportunities outside the classroom, (7) bridges to the next level (for example, through connections with industry), and (8) continuous evaluation (BEST 2004).
LOCAL INNOVATION NETWORKS
Innovation does not happen in a vacuum. The old stereotype of a lone inventor working heroically and single-handedly to come up with new creations never was accurate—Thomas Edison had an entire “invention factory” devoted to innovation. In today’s increasingly complex and interconnected world, innovation efforts are most likely to be successful in the context of innovation networks that connect innovators, investors, customers, workers with appropriate skills and talents, industry, academia, policymakers, regulators, and other stakeholders. Innovators in academia benefit from links to industry, and vice
versa, and both benefit from links to policymakers and regulators since government policies and regulations can have a tremendous effect on the prospects of innovations.
Elements of Innovation Networks
Innovators need access to a variety of resources if they are to develop their ideas into marketable products. They need access to low-cost capital, for instance—from investors who are willing to provide funding for projects that carry a certain amount of risk. Such investors are quite different from those who invest in established firms with less perceived risk, and they are not found everywhere.
Innovators need access to people in a broad range of disciplines. When they come up against a problem that requires a particular talent to solve—say, a machine-learning problem—they need someone who has that talent. And because most innovation today is interdisciplinary, innovators generally need people with a wide variety of skills. As Frans Johansson (2004) explained in his book The Medici Effect, a great deal of innovation is created by bringing together people with different experiences, competencies, and ideas, enabling the application of concepts or tools from one area to a totally different area, resulting in new insights and inventions.
It is also helpful for innovators to establish links with customers. As discussed above and in Chapter 2, feedback from potential customers is one of the best ways to hone an innovation and maximize its chances for success.
The ideal innovation network has all of these components, the players know what their contribution points are, and there are communication and information flows between the components. Investors should interact with academic contributors, the talent pool should interact with industry, and so on. Most successful innovation networks are local—the majority of the components are within a relatively small region—with connections to the broader global innovation ecosystem. The local network facilitates interaction—innovators do not have to look far afield to find what they need—and the outside connections provide links to resources that may not be available locally.
A highly effective example of an innovation network is Silicon Valley, where the synergy among participants has led to decades of innovation. The development of Silicon Valley in the region between San Francisco and San Jose can be traced to two main factors: the presence of Stanford University, with its graduates in the physical sciences and engineering, and a significant amount of military spending in the area on research and development. Once the innovation network got started with early players such as Hewlett-Packard (founded by two Stanford graduates) and Lockheed (located there because of military tie-ins), success built on success, and increasing numbers of innovators chose the area to pursue their dreams.
Outside of Silicon Valley, a number of other local innovation networks
have developed—in the area around Boston, the Research Triangle in North Carolina, the area around Austin, Texas, and the Seattle-Tacoma area in Washington. There are also well-established local innovation networks in Israel and Taiwan, among other places.
These networks are typically in areas with a large number of young people with scientific and technical skills, often associated with one or more upper-tier research universities in the area. There are examples of the formation of these networks in more rural areas such as Mondragon, Spain (MacLeod 1997) and Flanders, Belgium (NRC 2008) as well as the more typical urban setting. In Troy, New York, for example, there is a cluster of gaming companies because students and graduates from Rensselaer Polytechnic Institute (RPI) with an interest in gaming decided to stay in town and follow their passion. But, as Heather Briccetti, CEO of the Business Council of New York State, pointed out, the presence of a university is not enough. In the case of one RPI student who decided to turn his interest in gaming into a company, his innovation was supported by an incubator, which provided him with advisors and people with the necessary expertise to transform his idea into reality—a company that now employs 75 people.10
Innovation networks in metro areas can be facilitated by urban development decisions. Urban assets such as public and private spaces for stakeholder collaboration and transportation systems can facilitate the connections that stimulate innovation (Katz and Wagner 2014). Unfortunately, the structure of decision making at the state and local levels can make it difficult to support innovation networks. Christopher Cabaldon, mayor of West Sacramento, explained that coordination of local decisions on housing, transportation, zoning, and other urban development issues is needed to attract a critical mass of stakeholders and facilitate connections between them. But the way governments are organized, each department has its own specific mission that it won’t or can’t compromise even if it is in the state’s or locale’s interest to make tradeoffs among various objectives. The only way to change that, Cabaldon said, is to change how decisions are made, from this “functional approach” to a “place-based approach” in which decisions are coordinated to optimize a broad set of outcomes, such as quality of life, environmental sustainability, and economic growth, not just how many people are moved or housed. This coordination can allow state and metro area governments to ensure that the urban resources to enable innovation networks are present in the same location.
Coordinated decision making across metropolitan government silos has been implemented in Chicago and Denver (ICF 2009). In 2005 the Illinois state legislature merged the regional planning and transportation planning agencies
10 Remarks of Heather Briccetti at “Making Value for America: A National Conference on Value Creation and Opportunity in the United States,” February 27, 2014, Beckman Center of the National Academies, Irvine, Calif.
in the metropolitan Chicago area and the consolidated agency developed the area’s first regional comprehensive plan for land use, transportation, housing, human services, environment, and economic development. Going beyond traditional performance metrics of functional planning agencies, the agency is developing performance indicators focused on issues of quality of life, sustainability, and innovation.11
Developing Effective Innovation Networks
The development of a local innovation network requires more than the presence of a university, businesses, and a supportive local government. It requires intentional collaboration and assets to take advantage of the strengths of the local area in a deliberate way.
An example of the purposeful development of a local innovation network can be found in the efforts of New York State to create a network of chip manufacturing companies. It began with a suggestion by IBM, which is headquartered in Armonk, that the state work to attract cutting-edge semiconductor manufacturing capabilities. Because of its high taxes, New York is not generally seen as friendly to businesses, but with IBM’s prodding the state decided to try to attract the new business. The state does have a number of assets that make it attractive to companies—excellent universities, good infrastructure, proximity to markets, and a large amount of undeveloped land in the northern part of the state—and by offering subsidies it was able to offset the otherwise high cost of doing business there. The state convinced GlobalFoundries to build a major semiconductor factory in Saratoga County, and it now employs 4,000 people. The partnership between New York State, IBM, and GlobalFoundries, along with other actors such as SEMATECH and RPI, jumpstarted a New York–based innovation network centered on semiconductor chips (NRC 2013b).
In her presentation to the committee, Heather Briccetti offered some lessons about how best to develop innovation networks based on her experience with the development in New York State, citing three necessary components:
(1) The private sector must be involved in identifying where the opportunities lie and in creating local ecosystem value. Governments are generally not good at identifying local strengths and opportunities on their own; once a government starts choosing winners and losers, politics inevitably becomes involved and skews the process.
(2) There must be a strong educational system, both primary and secondary, to both attract over the near term and prepare over the longer term people who can contribute to the innovation network. Recognizing that, IBM has become a partner in a New York State project encourag
ing local companies to become involved in K–12 education in order to help fill their workforce needs.
(3) There must be partnerships between the private sector and government at the local level. It is not enough to get involved at the state or national levels: companies must work with local governments on local policies, such as education and infrastructure.
Unfortunately, these components are often lacking in many regions around the United States. In particular, larger companies seldom get involved in government partnerships at the local level.12
These key components have been instrumental in the development of effective innovation partnerships across the United States. Successful partnerships tend to be characterized by industry initiation and leadership and public commitments that are limited and defined (NRC 2002). In addition, it is important for these partnerships to have clear objectives, cost sharing arrangements, and sustained evaluations of measurable outcomes to support learning and improvement.
Networking among various innovation stakeholders—entrepreneurs, investors, researchers, federal laboratories, local government actors, and others—is critical to innovation networks. Effective leadership and professional management to facilitate this networking have underpinned the development of innovation networks in the Research Triangle, the Sandia National Laboratories region in New Mexico, and the NASA Research Park in California (NRC 2009). Successful networks have been developed abroad as well by leveraging this type of networking. For example, an initiative spurred by the federal state of Brandenburg, Germany, in 1999 provides young entrepreneurs with a mix of individual face-to-face support by a business advisor, group learning workshops, and experience in a business incubator—and led to the support of over 300 startups by 2009 (OECD 2009).
More recently, the US government has begun investing in a series of institutes for manufacturing innovation with the goal of creating a network of regional manufacturing hubs. These institutes, coordinated by the Advanced Manufacturing National Program Office, serve as a point of private-public collaboration for suppliers, schools, colleges, and other organizations to develop and scale particular manufacturing technologies and processes (EOP 2014).13 As of January 2015, six institutes have been launched in different regions of the country focusing on additive manufacturing, digital manufacturing and design,
12 Remarks of Heather Briccetti at “Making Value for America: A National Conference on Value Creation and Opportunity in the United States,” February 27, 2014, Beckman Center of the National Academies, Irvine, Calif.
lightweight materials, next-generation power electronics, integrated photonics, and advanced composites.
The ultimate goal of a local innovation network is to link companies, investors, academia, workers, and government to work together in supporting the creation of new value.
FLOW OF CAPITAL INVESTMENTS
Innovation requires investment. Companies need funding for research and development, capital investments, marketing, and other costs associated with creating value. Yet the evidence indicates that, although many promising opportunities for value creation are opening up, several factors are preventing corporate and venture capital investments in these ideas.
The rate of corporate investments has slowed in recent years. One way to gauge corporate spending on investment is to calculate the value of corporate profits minus current investment. For many decades that number was approximately zero, meaning that, on average, corporate investments were about equal to corporate profits. In the past decade, however, the number rose above zero as corporations spent less on investments relative to their profits. Corporate cash balances have risen to record highs, exceeding $2 trillion in domestic reserves by September 2014 (Carfang 2014).
This situation has led some researchers to wonder whether corporations have run out of ideas to invest in. In The Great Stagnation, Tyler Cowen (2011), an economist at George Mason University, argued that for centuries the US economy advanced by taking advantage of “low-hanging fruit”—a continent’s worth of land to expand into, the labor and contributions of immigrants, and powerful new technologies such as agricultural machinery, the locomotive, and electrical power. With little low-hanging fruit left to harvest, the United States is now in a decades-long economic stagnation, Cowen says, and future innovation will require a very different approach than sufficed in the past.
But many of the largest US companies argue that there is no shortage of problems to solve or of ideas for solutions to them. IBM, GE, Boeing, Apple, and others have a wealth of ideas for potentially valuable innovations—far more than they actually pursue. Why aren’t they pursuing them? Why have corporate investments dropped relative to corporate profits? Why are companies sitting on record amounts of cash?
Chris Johnson from GE Global Research pointed to two factors in particular that can slow very large investments: regulatory risk and preferences for
short-term returns, especially in the face of stockholder expectations.14 The first factor relates to uncertainty about future regulations, such as environmental, tax, and fiscal policies, that affect longer-term transactions. For example, the tax credits businesses receive for qualified research expenses expire every two years and must be renewed by Congress, adding significant uncertainty to long-term research expenditures. Regulatory uncertainties increase the risks of investments, and so businesses tend to hold cash as a precautionary measure (Bates et al. 2009).
The second factor relates to a preference for investments that lead to short-term gains over those that pay off in the longer term. In today’s financial market, stockholders demand steadily improving performance each quarter. The current tax structure encourages stockholders to hold their assets at least one year by providing a lower tax rate for these investments, but there are no incentives for holding stocks over longer periods. As a result, managers feel pressure to produce short-term earnings to boost their quarterly financial reports, leading to myopic behavior (Bhojraj and Libby 2005; Stein 1989).
The combination of these factors has led businesses to limit the risk associated with investing in transformational “bets” and thus to refrain from pursuing potentially profitable projects that would produce new factories and new jobs. The companies have the cash and financing to invest in these projects but often choose to focus on incremental improvements and short-term projects instead.
The focus on short-term returns has impacted investments not only in today’s products but also in emerging technologies that could lead to entirely new industries. A vivid example of earlier long-term research and development, with a horizon on the scale of decades, was the work done at AT&T Bell Labs that led to the invention of the transistor in 1947—and was the basis for the digital industry that exploded over the past 30 years. There were other examples of forward-looking research after World War II at a number of industrial laboratories—for example, IBM’s research labs and Xerox’s Palo Alto Research Center—as well as national laboratories. Although businesses are still investing in long-term applied research and development, the commitments may not be robust enough to support the explosion of innovation needed to lead US value creation in the coming decades. Moreover, there are concerns that the shift in industrial R&D investments away from fundamental research, such as the work carried out in Bell Labs that led to unexpected transformative discoveries, in favor of applied research with foreseeable results threatens the United States’ technological strength (Narayanamurti 2013; NAE 2005).
14 Comments by Chris Johnson at a meeting of the Foundational Committee on Best Practices for the Making Value for America Study, September 23, 2013, the National Academies, Washington, DC.
Long-Term Decline of New Business Activity
Given concerns that large corporations are underinvesting in long-term research, one might turn to new businesses to look for emerging technologies that could drive innovation in the 21st century. Unfortunately, the rate of new business creation has been in a longstanding decline in the United States and the ability of these businesses to access the capital required to commercialize innovations is in short supply.
Entrepreneurial activity in the United States has been declining for the past 30 years, a worrisome trend for jobs because new businesses (startups) are critical for job creation. Older businesses are a key part of the economy—they employ most Americans and are important contributors to productivity growth—but historically they have tended to eliminate as many jobs as they create (Decker et al. 2012). New businesses, on the other hand, create jobs. In fact, businesses as young as five years or less accounted for all job growth between 1982 and 2011 (Haltiwanger et al. 2013). Although many startups don’t survive, among those that do are a small group of very fast growing businesses that account for an outsized portion of the job creation and innovative effort taking place in the economy (Decker et al. 2014).
One way to assess the level of US business creation is to look at the number of startups launched each year with at least one paid employee and compare it to the total number of workers in the United States. In 1980, there were more than 35 startups created for every 10,000 workers. By 2010, the number had been cut in half to only 17 (Lynn and Khan 2012).15 Another important measure of business creation is the share of businesses in the United States that are younger than five years old. This share declined from almost 50 percent in 1980 to less than 35 percent in 2010 (Haltiwanger et al. 2012). This decline is occurring across the value chain, in manufacturing operations, services, and retail.
Considering the important role of new businesses in creating jobs, the decline of business creation in the United States raises concerns about the pace of job creation. The number of jobs created by businesses less than one year old decreased from 4.1 million in 1994 to 2.5 million in 2010.16 It is important to note that the causes of this slowdown are not known. While not all possible explanations imply severe consequences for the US economy, research has linked the slowdown
15 These statistics do not include the creation of businesses without any paid employees and have been criticized as inappropriate measures of entrepreneurship because they include individuals who claim self-employment because of a lack of job opportunities (Earle and Sakova 2000). Measures of all new businesses, with and without employees, show that the number established each year has been roughly constant; the Kauffman Index of Entrepreneurial Activity shows that the share of people who established either an employer or nonemployer business has fluctuated around 0.3 percent since 1996 (Fairlie 2014).
16 Data from the Bureau of Labor Statistics website, Employment Dynamics, Entrepreneurship and the US Economy (www.bls.gov/bdm/entrepreneurship/entrepreneurship.htm; accessed August 12, 2014).
in business creation and, more generally, business dynamism—the process by which businesses continually are born, fail, grow, and contract—to declines in productivity growth, innovation, and employment, especially for younger and less educated workers (Acemoglu et al. 2013; Davis and Haltiwanger 2014). John Haltiwanger and his colleagues (2012), using Census Bureau statistics to analyze the role of startups in US job creation, attribute the slowdown of business dynamism to the combination of a long-run secular decline and a short-term accelerated decline caused by the recent recession. The authors explain the important role of new businesses in job creation and the consequences of allowing the current decline to continue (Haltiwanger et al. 2012, p. 2):
In 2010, 394,000 startups created 2.3 million jobs (these were not simply establishment openings but new firms whose establishments also were new to the economy). This reflects substantial job creation in a time of anemic overall economic activity. Over the same period from March 2009 to March 2010, the net job creation from all US private sector firms was −1.8 million jobs. Without the contribution of business startups, the net employment loss would have been substantially greater.
These are longer-term trends than the recent economic recession, and they are likely to continue even after economic recovery unless actions are taken to ensure that the United States establishes new ways to make value.
Lacking access to long-term, low-cost capital, many potential startup companies with valuable technologies originating in universities and laboratories cannot bring them to market.
Lack of Capital for New Startups
MIT researchers examined the availability of capital for early and later-stage startups in the United States in a 2014 report, Production in the Innovation Economy. They found that entrepreneurs face a critical stage of growth once they are ready to move into the pilot phase and early commercialization, when significant capital investments are needed but not available in the United States. In many cases, strategic partnerships of multinational corporations and foreign governments provide the necessary capital and acquire the startup or pull it overseas. The authors identify this lack of capital in the United States as “the critical juncture where innovations developed in the United States are lost,” which hinders the creation of significant downstream activities such as manufacturing (Locke and Wellhausen 2014, p. 10).
Researchers in universities and federal laboratories across the United States face this difficulty in accessing financial support to commercialize their innova-
tions. While it is somewhat easier for researchers at a few major universities (Stanford and MIT, for example) that have good connections to investors to find the financial support to develop their technologies into viable businesses, most do not have access to these resources. Chris Silva of Allied Minds, a company that commercializes discoveries from university and federal laboratories, described the impacts of this reality to the committee.17
Lacking long-term capital, many potential startup companies with valuable technologies originating in universities and laboratories cannot bring them to market. Silva estimated that at the 40 universities and 40 federal government labs that Allied Minds works with, there are at least 2,000 inventions a year that are potentially commercializable, but Allied Minds has the resources to help launch only six to ten companies a year. And, unfortunately, it has very few peers in the United States that focus on supporting startups to commercialize these technologies from universities and federal labs (Ford and Nelsen 2013). Similar companies, such as IP Group and Imperial Innovations, exist in the United Kingdom, but nowhere else (Moran 2007).
The lack of capital for researchers and entrepreneurs interested in commercializing a new technology is exacerbated by a transition in venture capital in the United States. During the 1990s venture capital provided much of the funding for the countless startups and early-stage companies that yielded dramatic growth in high-tech innovations in Silicon Valley and other regions of the country. Since then, however, venture capitalists have largely abandoned longer-term areas such as biotechnology in favor of funding businesses that are much further along, have less risk, and go up in value every year, if not every quarter (NVCA 2013).
Capital shortage is particularly damaging for innovation in areas such as energy, biotechnology, and materials science. Companies in these capital-intensive long-term fields require patient capital investments with longer time horizons. It may be eight, ten, even twelve years before they begin to fully realize their value, and with the current emphasis on short-term profits that is simply too long. Thus in biotechnology and energy, for example, the money flowing into early-stage companies has essentially collapsed, creating a hole in the innovation pipeline (Margolis and Kammen 1999; NVCA 2013).
Federal programs such as loan and investment programs in the Department of Commerce’s Small Business Administration (SBA) have acted to provide some funding for longer-term research and commercialization projects that may not otherwise be supported by venture capital (NRC 2009). However, while this financing was previously directed primarily at startups, it has shifted to fund
17 Remarks of Chris Silva at “Making Value for America: A National Conference on Value Creation and Opportunity in the United States,” February 27, 2014, Beckman Center of the National Academies, Irvine, Calif.
older companies, which are less likely to generate significant growth in employment or sales compared to younger companies (Brash and Gallagher 2008).18
To operate efficiently, businesses across the value chain must have access to reliable energy and natural resources, transportation, and communication systems. Increasingly, many businesses also need access to computational and digital resources such as high-performance computing grids and information storage.
INFRASTRUCTURE THAT ENABLES VALUE CREATION
In addition to an innovative workforce, capital, and best practices, creating value requires a suitable infrastructure. Without access to appropriate energy sources, transportation, and reliable communications, any type of business will be at a disadvantage. Poor infrastructure hinders value creation.
Contributions of Infrastructure to Innovation
History has shown that the creation of new infrastructure generally leads to technological disruption and massive innovation. The creation of the Internet is one of the best-known examples from recent decades: it has enabled everything from email and Internet shopping to social media sites and home appliances that can be controlled from a distance. The 19th century saw the development of a nationwide railroad system, and the early 20th the distribution of electricity, telephony, the national highway system, and the availability of clean water, which kept the populace healthy. Thus the construction or upgrading of infrastructure can be an important and effective way to encourage innovation and value creation.
To operate efficiently, businesses across the value chain must have access to reliable energy and natural resources (electricity, water, gas, etc.), transportation (via roads, rail, air, and water), and communication (telecommunications, Internet, etc.). Increasingly, many businesses also need access to computational and digital resources such as high-performance computing grids and information storage.
From an economics perspective, the development of infrastructure is often best planned and paid for by government because of positive externalities—public benefits that accrue to those who did not pay for it. Thus one government policy that is most likely to improve the nation’s ability to create value in coming years is to support the development of infrastructure. Government
should think of infrastructure in broad terms—not just the physical infrastructure but also education to produce more skilled workers and the establishment of networks that encourage communication and linkages among the people and institutions involved in innovation.19
The United States has many infrastructure assets that facilitate innovation and value creation. Its research infrastructure—the universities, laboratory facilities, and high-performance computing resources that enable cutting-edge research—is widely considered the best in the world (NRC 2012a). Compared to many other countries, the United States also has plentiful access to energy that is relatively cheap and available almost anywhere in the country, especially with the recent surge in the supply of domestic natural gas from shale deposits (although several other countries are considered to have a more reliable electricity supply) (WEF 2013). However, several areas need significant improvement.
The American Society of Civil Engineers issued a “report card” in 2013 that gave the overall state of US infrastructure a D+, based on poor performance across almost all infrastructure categories covering transportation, water, waste, energy, and schools. Only solid waste management received a grade as high as B−. Roads, water, aviation, transit, and levees all received a D or D− (ASCE 2013).
In 2014 the World Economic Forum (WEF), in its report on global competitiveness, scored countries around the world on the quality of their transportation, electrification, and telephony (Schwab and Sala-i-Martín 2014). The infrastructure factor on which the United States scored highest was the number of available airline seats, for which it was ranked best in the world (although its overall air transport infrastructure was ranked 18th). On the other hand, it was 30th in quality of electricity supply, and 18th in both landline telephone communications and quality of roads. The picture is substantially worse for modern communications infrastructure in the United States: WEF ranks the country 95th in mobile communications and 35th in Internet bandwidth, behind Australia, Barbados, Hong Kong, and much of Western and Eastern Europe.
Improvements in Traditional Infrastructure
One of the best ways that the United States can encourage the creation of value is to upgrade key aspects of its infrastructure. Limitations to the US transportation infrastructure, for example, hurt productivity and result in large costs to the economy. The economy lost an estimated $22 billion from airport congestion and delays in 2012, $90 billion from deficient transit systems, and $101 billion of wasted time and fuel from traffic congestion (ASCE 2013). The
19 Comments by Chad Syverson at meeting of the Foundational Committee on Best Practices for the Making Value for America Study, September 23, 2013, the National Academies, Washington, DC.
nation’s port systems, which are critical for the transportation of goods, are also in need of improvement.
Another area that is attracting attention is improvement of the reliability and efficiency of the electricity system. There is work being done, for instance, on the development of smart microgrids; these are much smaller versions of the current centralized systems for generating and transmitting electricity, and they are “smart” in the sense that electrical supply and demand are constantly monitored and regulated to maximize efficiency (Berkeley Lab 2014). To the extent that these microgrids lead to a supply of electricity that is more reliable, more efficient, and greener than the traditional electrical supply, their development could be a competitive advantage for the United States.
Improvements in Information, Communications, and Computing Infrastructure
Generally speaking, any infrastructure improvements that increase the ability of people to communicate and interact are likely to improve the nation’s ability to create value.
The committee concluded that one of the most important infrastructure improvements that would enable future value creation in the United States is access to high-speed Internet—particularly wireless—and high-performance computing. As described in Chapter 2, advances in computing power are driving improvements across manufacturing value chains: computer modeling and simulation capabilities increase production efficiency, reduce the need for expensive physical prototyping and testing, and increase quality and reliability along the value chain. Advanced computing capabilities are also enabling entirely new types of products and services. Many of the emerging technologies and capabilities described in Chapter 2—such as data collection, social media analysis, and autonomous vehicles—depend on sophisticated computing capabilities.
Companies and communities are starting to invest in measures to enhance access to high-speed computing capacity. Google operates ultra-high-speed “fiber” services in three US cities—Kansas City, Provo, and Austin—and is considering building such networks in nine more cities (Finley 2014). These services carry data at a gigabit per second, or about 100 times faster than today’s typical Internet connections, through a fiber-optic connection directly to customers’ homes. Other US municipalities have also installed or are planning to install such ultra-high-speed networks, either themselves or by enlisting a company to build them (Kopytoff 2013). And Google is working on technology that will make it possible to send data across a network at 10 gigabits per second, 1,000 times faster than the typical Internet connection today (Wilke 2014).
One of the most important changes needed to improve wireless is the modernization of spectrum allocation. Use of mobile devices that rely on wireless
data and calls has been growing rapidly, but the capacity for these transmissions has not increased because there is little available spectrum to carry them (Rosston 2013). There is, however, significant opportunity to more efficiently allocate spectrum. Spectrum allocation has historically been assigned in an ad hoc manner and could be improved by repurposing the pool of spectrum to make more capacity available for use by high-demand applications such as mobile broadband (Bennett 2012).
Access to reliable, high-speed networks and high-performance computing is essential to improve connectivity and ensure reliable production and service, cornerstones of innovation and value creation.
FEDERAL PROGRAMS THAT MONITOR THE VALUE CHAIN
A variety of federal agencies and programs track the performance of various activities in US-based manufacturing and high-tech service value chains. Two of the most prominent statistical agencies are the Department of Labor (DOL) and Department of Commerce (DOC). The DOL’s Bureau of Labor Statistics collects and publicizes labor market information such as employment, pay and benefits, and labor productivity. The DOC’s Bureau of Economic Analysis publicizes economic accounts statistics such as gross domestic product, input-output tables, and trade in goods and services. Also housed in DOC, the Census Bureau collects additional information on trade, employment, wages, and a variety of industrial operations, including best management practices. Each of these statistical agencies conforms to the North American Industry Classification System (NAICS), which divides the economy into manufacturing, transportation and warehousing, wholesale trade, retail, professional and business services, information, and ten other industry sectors.
NAICS is particularly important because many datasets, such as the census and economic accounts, rely on this classification system to support government policymaking and inform the American public about the condition of US industries and the overall economy. These datasets are the lens through which policymakers and economists view industrial activity and therefore have a profound influence on the government’s and public’s understanding of the economy (Dalziel 2007). Statistics based on NAICS are used to monitor the economic status of the United States, determine businesses’ eligibility for particular tax exemptions and government contracts, and determine which businesses are subject to certain regulations.
NAICS organizes economic activity based on how establishments carry out their activities, rather than the purpose for those activities, and thus has the advantage of grouping activities that have similar production processes. But it ignores relationships among activities along the same value chain, which traverses the production of goods, services, and software. For example, the system groups automotive and pharmaceutical manufacturers together and
the production of automotive electronics with electronic medical equipment, while ignoring the relationship between automotive manufacturers, vehicle electronic component suppliers, dealers, and automotive repair shops.
The fact that the industry classification systems do not account for value chains is increasingly a problem as the use of software, electronic components, and services is becoming more important across a variety of industries, as discussed in chapter 2. The types of goods and services required to meet a particular demand, and how they are produced, have changed enormously since NAICS was established in the 1990s, but the convention of organizing economic metrics by means of production is not flexible to these changes. As a result, a large portion of US economic activity is accounted for by “unmeasurable sectors” (such as the app economy), which are not monitored (Mandel 2012).
Arranging economic statistics instead by the systems of activities along value chains would allow a representation of the economy that reflects the ways companies organize themselves into clusters and sectors (Dalziel 2007). Moreover, it would be less vulnerable to changes in technology than the current approach. Such a classification would also facilitate an understanding of how regulations, economic forces, and other stimuli propagate through interrelated segments of the economy.
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