 I want to begin not with directly with the question in the title but with a couple of questions that political leaders everywhere ought to be asking themselves. What are the choices that we have to build an economy that is productive and competitive and that provides opportunities for people in all parts of our societies to do well? How can our communities, often with very few economic resources of their own, at the very least survive and in the best case prosper in the global economy? Now, unfortunately, these aren't the questions that our politicians spend most of their time debating, mostly they're preoccupied with trying to manage the immediate problems of the economic cycle. But when the recession recedes in memory, as it surely will, the longer term questions will remain and we'll still be looking for answers. Two of the answers have now become the conventional wisdom, that we have to accelerate the transition to the knowledge economy in which the dominant activity is the creation of ideas and knowledge and innovative products and services. And second, that we must look to higher education institutions to help with this transition because universities are the key source of the discoveries and there's technically trained people that are the most important assets in the knowledge economy. Moreover, universities, unlike almost every other kind of economic organization, tend not to move. They're committed to their regions in a way that many of the traditional stalwarts of local and regional economies, the banks, the insurance companies, the old manufacturing firms and so on, no longer are. So universities everywhere are being asked and urged to embrace the third stream mission of local economic development by promoting patenting and startups and so on. But about both of these answers, there are some points where the conventional wisdom may need a little bit of tweaking. For example, innovation isn't only about entrepreneurship and new business formation. It's true that the role of entrepreneurial firms has become progressively more important in innovation, especially in the last decade, and that universities have become more visible as incubators of these startups. But that's not all there is to it and there's a larger system within which innovation occurs. At MIT, we have many visitors who want their own universities to become drivers of economic growth and they come to Cambridge to see what's happening. But sometimes they only seem to see what's on the surface. They see the startups that are clustered around the MIT campus and all the new office buildings and lab spaces. And when it's pointed out to them that that's just a part of the larger innovation system, they say, yes, yes, please show us your technology licensing office and tell us about your business plan contests. But even that is still just a relatively narrow slice of the innovation system and there's much more of it that's invisible, kind of like the submerged part of an iceberg. And I'm going to say a bit more about that part in a few minutes. Another misconception is about how long it takes to build an effective innovation system. My research team at MIT is currently doing some work in Brazil. And things aren't going terribly well down there at the moment, as you know, there's a political crisis and the economy is doing poorly. But on innovation, where historically the Brazilians have struggled, they've been doing some good things over the last few years. They've been investing in the university system, they've been investing in their national labs and there's a much stronger dialogue between government and industry than before. But people there are complaining. Look, they say, it's not working. There isn't enough patenting. There aren't enough startups. But really, it takes more than a few years to see these kinds of outputs. And if you look, for example, at the life sciences sector in the Boston area, we've recently woken up to the fact that it's now probably the strongest such cluster in the United States. But that's the opposite of an overnight success story. It took decades to build and the single most important factor was the sustained flow of basic research funds from the federal government to the excellent universities and teaching hospitals in Massachusetts. Of course, the venture capital community and the culture of entrepreneurship have also been important, but the sustained federal commitment to supporting outstanding researchers has been by far the most important factor and it took decades to bear fruit. There are also some other misconceptions about the economic role of the university. Here's a view that the bell towers of academia have replaced smokestacks as the drivers of the American urban economy. Now, if you take a step back, that's actually quite a remarkable statement. It says that universities not only have to fulfill their mission as educational institutions, which many of them are struggling with, but that they must also be the salvation of our struggling economies. It's a nice thought, but I'm afraid it's a rather unrealistic one. If you consider, for example, the question of startups, sometimes it seems I, of course, talk to a lot of students and sometimes it seems that every one I talk to is working on a startup, sometimes more than one, and it's terrific to see that activity. But it's also important to recognize that the number of university-related startups is only a very small fraction of the overall rate of new business formation. Even with the most generous accounting, it's almost certainly no more than two to three percent of the total in the U.S. And if we consider only the startups formed around university-generated IP, the fraction is less than one in a thousand. Similarly, the rate of patenting by universities is less than three percent of the overall rate of patenting in the economy. And even the most prolific university campuses from a patenting point of view, places like Stanford and MIT, receive a total number of patents that's modest by corporate standards. In fact, if Stanford and MIT were companies, neither of them would be ranked in the top 100 patenters in the United States. None of that is to say that university-related patenting in new business formation aren't important. And of course, in a few fields such as biotech, the impact is much higher. But we do need to keep all of this in perspective relative to the growth and job-creating capacity of the economy as a whole. A second misconception relates to the economic payoff to universities themselves. In fact, the expected financial return to universities from tech transfer is low. For U.S. universities as a whole, licensing income is only a small fraction of research revenue, somewhere between four and six percent, and we'll never replace research revenues as a—excuse me—research grants and contracts as a source of revenue. For example, at MIT, we receive about $80 million a year in revenues from licensing royalties and other intellectual property. And that's nothing to sniff at. But it's still only five percent, roughly, of the total MIT research budget. So the point is that technology transfer provides many benefits, but we shouldn't expect it to transform the finances of the university. And we absolutely shouldn't expect that income on intellectual property can be a substitute for direct research funding. In fact, the licensing of intellectual property is just one of many ways in which knowledge flows out of universities into industry, and almost certainly the most important of those ways is recruiting. There's an old adage, many of you probably heard it, that the best form of technology transfer is the moving van that transports the new PhD from his or her university laboratory to a new job in industry. And according to Gordon Moore, one of the founders of—or co-founders of Intel, this is even true in the land of Google and Facebook. And all of this suggests that we need a more holistic view of the university's role in the economy and in innovation. And this in turn also requires a more complete picture of the local innovation systems of which universities and other higher education institutions are part. Sometimes they're a small part, sometimes they're a large part. In our own research at the Industrial Performance Center, we found it helpful to think about innovation in terms of four separate processes. First—so here, of course, this is my definition, it's a very simple definition, but that's my definition of innovation. The application of new knowledge to create values, not invention. If you've invented something but you haven't done anything with it, that's not innovation. It has to create value for someone or something. But here are the stages of innovation that we found useful to differentiate. First of all, the discovery of and development of new technologies or new business models, let's call this option creation. Second, determining the viability of these options in the marketplace. You can think of that as demonstration. Third, the cost reductions and other improvements together with the development of complementary infrastructure like manufacturing systems or regulations or, importantly, education and training that occurred during the initial period of take-up. Then finally, the learning-driven improvements that occur as innovation is adopted on a large scale. The picture here might look similar, but in fact this representation is very different from the traditional linear view of innovation that starts with basic research in the lab, followed by applied research and then development and then demonstration. In the first place, it's not a linear process. Knowledge and information flows up and down the chain in both directions. Second, what I've called option creation isn't the same thing as basic research in the lab. In fact, ideas for innovation can occur anywhere along the value chain. For example, in manufacturing plants among groups of users and in all kinds of other places. And basic research can provide valuable knowledge at each stage of this process, not just at the front end. And third, innovation doesn't stop at the point of commercialization. In fact, the continuing improvements that are made to a technology throughout its lifetime often have a cumulative benefit that greatly exceeds the initial gains that are achieved when the technology is first brought to market. But the most important thing about this picture is that a healthy innovation system is one in which all four stages of the innovation process are operating effectively and where there are strong handoffs between them. And here, there are at least three kinds of gaps or traps to worry about. One of these is the capital gap. We've heard a great deal about the valley of death between the lab and the first implementation of a commercial product or service, and that's important. But what's also important and often a bigger challenge is to raise the capital that's required for scale-up. And partly, this is because the capital requirements here are much larger, very roughly. They increase by an order of magnitude as you move from left to right, each step, in fact, from left to right. And even though the investment risks are lower at the front end, at the latter end than at the front end of the process, they're still too great for traditional forms of project and corporate finance. But the knowledge required for effective investing at these later stages is too deeply rooted in market conditions to rely on government-led decision making. This is a problem that varies by sector, and in some cases it doesn't seem to be a problem at all. But in other sectors, including, importantly, energy and clean tech and so on, it's much more serious because here the amount of capital that's required to achieve scale is often particularly large, and the time to profitability is long. And also, policy and regulatory risks are well above average. The second gap is about geography. In innovation, proximity tends to matter, and there are benefits to locating manufacturing close to R&D and design, especially when manufacturing technologies aren't yet mature and when product technologies are still evolving and don't lead themselves to modular design. We know that when manufacturing moves overseas, innovation has a tendency to follow. The third gap is a bit harder to explain. Basically it occurs at the interface between option creation and demonstration, and it has to do with the very different management skills and styles that are required for successful management of those two stages, and the difficulty of transitioning from one to the other. By the time we get to the point of demonstration, the most important activity is basically problem solving. Anybody who's been trained as an engineer understands what's involved in problem solving and what it takes to manage it. You define your goal, you figure out the resources you need to achieve that goal, and also the constraints on time and money and people. And then you divide the problem into its component parts, and you assign each part to the appropriate specialist, and the specialist solves the parts of the problem, and then you put them together, and you try to do all of this as quickly as possible. That's the essence of the problem solving method, and the basic approach is an analytical one. But at the prior stage, at the option creation stage, before you figured out what the problem is that you're going to solve, it's very different. It's more like a freewheeling, open-ended conversation, sometimes literally between customers and suppliers, or between designers and manufacturers, or between researchers and just about everybody else. And the goal of the manager at that stage is to get the conversation started, and to seed those conversations with good ideas and interesting people, and to keep the conversations going. We call this interpretive management to distinguish it from the analytical approach. An analytical manager is a strong, decisive leader, a problem solver. But the challenge for the interpretive manager is very different. This is a quote from the American movie director, Robert Altman, who talked about what his approach is to directing a scene. He says, I'm looking for something I've never seen, so how can I tell them what to do? And the key here for the manager or the movie director isn't to get closure, but rather to get the conversation started and then to keep them going until good things start to happen. And so these are two vastly different roles. We liken the second role to be the sort of thing that the hostess or hostess of a cocktail party is doing. It's actually very difficult to think in these two modes simultaneously. And yet the key to building and sustaining a creative company, and the same is true of a successful regional economy, is to create an environment in which both of these core processes of innovation, problem solving on the left side and interpretation on the right side, can take place in parallel. And there's another challenge. And that is that problem solving thrives in a competitive environment. To get rapid, effective problem solving, there probably isn't anything better than having efficient, well-functioning markets. But especially during the early stages of developing genuinely innovative products or services when things are ill-defined or ambiguous, you need sheltered spaces where the new possibilities can be explored, where it's possible to have conversations about half-formed ideas before anybody has figured out exactly what the product or service is that's to be commercialized, or even what the problem is that is to be solved by that new product or service. And as competitive pressures have built up in our economies, the scope for conversation and interpretation has narrowed. Places like in the U.S. Bell Labs and many of the other great corporate research labs where this sort of thing used to happen have been shut down or have been converted into something else. And so we need new spaces for this. And today one of the most important such spaces is the research university. In addition to their role in fundamental research, it's at these universities that companies can participate in the open-ended explorations of new technological and market possibilities from which ideas for new products and services are sometimes hatched. I have an example. My colleague, Professor Phil Sharp of our biology department, predicts that the next revolution in life sciences, which will facilitate advances in agricultural productivity, biofuels, ecosystem engineering and healthcare, will require the integration of the life sciences with the physical sciences, engineering, and the social sciences. And he predicts that this will occur on university campuses along with the convergence of scientific discovery, invention, and entrepreneurship. The next thing I want to talk about is scale-up. This is a topic that has received, and by that I mean scale-up of business enterprises. It's received a lot less attention from policymakers and researchers than how to, who are interested in economic development than how to cultivate startups. And yet when it comes to the health, local, and regional economies, the ability to grow larger-scale companies is no less important than the process of new business formation. Here's a slide that paints a picture of the role of scale-up in Massachusetts and California. There are about 2,000 companies here in more or less evenly divided between four groups of industries, advanced manufacturing, computer-related life sciences, and software and internet. And the companies in these four industries account for about 50% of all companies that have gone public in Massachusetts, I should say, over the last 25 years. And in the last five years have accounted for all or 90% of all venture-backed startups in the United States. So this is a fairly large portion of the technology sector. The 2,000 or so companies here includes every company that went public between 1980 and today in these four industries in those two states. And each data point represents a year of revenues for a company. And the number of people that were employed by that company at the midpoint of that year. Clearly there's a clear correlation here between employment and revenues, but as you would expect, a lot of scatter. And the suggestion of non-linearity in the software and internet case is quite interesting. Now why an individual company would want to gain scale is, I think, self-evident. But what about the benefits to a region? Again, it goes without saying that every region would welcome the job-creating benefits of a rapidly growing economy. But if you were given the choice between $110 million companies, $100 million companies, and $1 billion company, which one of those options would you prefer? Other things equal, assuming they all had the same number of jobs. Well we can't run the experiment, but it is clear that large companies provide certain benefits to regional economies that small companies, even lots of them, cannot. For example, in addition to jobs, which in this little thought experiment I'm holding constant, is management talent. A larger company provides a training ground for senior managers that deepens the region's managerial talent pool. It's a valuable resource for the regional ecosystem, and as one of the companies we, CEO, or one of the companies we interviewed said, as companies grow there are positive externalities to the region. Several senior executives from our company have gone on to other companies and taken a leading role there. This recycling of talent helps smaller companies in the region grow to scale. And similarly for entrepreneurial role models, entrepreneurs who grow companies to scale themselves become role models for other entrepreneurs who see the possibilities and may as a result set their sights higher than they would have otherwise. And scale companies themselves can serve as role models. As one of the other CEOs said, if there are five to ten companies in the area that have scaled up, it changes the perspective of everyone. Large companies also serve as anchors for regional clusters, providing jobs and training, R&D, and infrastructure investment, as well as generating spin-offs and other knowledge spillovers. So what do we know about how companies stop being startups and start growing to scale? And is there something about a scale-up ecosystem that's different from a startup ecosystem? Well, every company is different, but somewhat arbitrarily we divided the scale-up process into three stages, each presenting a different set of challenges from an innovation point of view. Some companies in the startup phase are focused on technological innovation, developing new markets, raising capital, and recruiting talent. Talent is, technical talent is critical at this stage and recruiting the right team to help get a product ready for the marketplace is a top priority. And raising capital is more or less a non-stop activity. Many important decisions that are taken during this phase shape the future growth trajectory of the company. For example, the form of capital that's sourced. It may last for ten or more years for some companies, but for software and internet-related companies, this is often a much shorter period. In the second scale-up phase, the company's product has been established in the marketplace is achieving some revenue success, although the company may still not be profitable, but the team that helped the company get up and running and that developed the core product typically needs to be augmented or even replaced at this stage of scale-up. These internal functions start to become important. Things like human resources, customer relations, supply chain management, and new senior management may need to be hired that who have experience running a company of scale. Often companies turn over their senior management several times during this period. New rounds of financing are needed. The company may even consider an IPO at this stage. Another key issue during this phase is the challenge of balancing innovation with execution, with innovation often taking a backseat to execution. In the third stage, many of the challenges of growing to scale have been overcome, and companies are focused on execution. But another challenge is to maintain capabilities and space for innovation in order to develop the next generation of products and technologies. So companies that are negotiating these transitions employ a variety of strategies. Some are built to sell, and they don't even think about going beyond the start-up phase. But others are headed by leaders who are committed to building firms of scale. Some of those rely exclusively on organic growth, while others obtain key capabilities through acquisition. Acquisition activity can cut both ways for a region. On the one hand, innovative local companies that are acquiring other firms to gain scale and expertise will enhance their impact on the regional economy as they grow. On the other hand, if smaller innovative local firms are themselves acquired, it might be good for the company executives and investors, but it may be less good for the region. In the best scenario, the acquired company stays in the region. The innovation continues to be pursued within the acquired firm with positive spillovers in talent and technological development, and then larger amounts of capital are available to pour back into the innovation ecosystem through new ventures. And we've seen that nice scenario happening repeatedly. But there can also be a downside, especially if the acquired company's capabilities are relocated elsewhere. This is a chart, it's a busy chart, you probably can't make much of it, but it's showing who has been acquiring Massachusetts companies that have gone through an initial public offering in the four industries that I mentioned before. The disproportionate number of acquisitions by California firms of Massachusetts firms in software and internet and also computer-related areas is of some concern and suggests the possibility of a long-term shift that may negatively affect the ability of our regional economy to realize the downstream economic benefits of our innovative capabilities in those areas. The situation in the life sciences maybe seems to be a bit different in this regard. So finally, let me come back to the role of higher education institutions in regional economies. Earlier, I called for a broader view of this role, augmenting what's now become the standard model of university-initiated technological entrepreneurship with other important functions including providing spaces for the open-ended interpretive conversations where ideas for new innovations are born. But now I want to call for an even broader view which takes account of the different patterns of innovative activity that may be going on in the regional economies in which these institutions are located. Several years ago, at the industrial performance center, we conducted a study that was designed to capture some of this diversity. We identified 24 different locations, most of them city regions, in six countries and in each location we focused on a particular industry and on the change in the mix of products and services produced by that industry over a period of time which ranged from 10 years to 30 years. And we were particularly interested in the contribution, if there was one, of local universities to these transitions in industries or in the products they produced. As you can see, the sample includes mature industries like cars and industrial machinery and newer or emerging fields like optoelectronics. It includes relatively prosperous regions like the Boston area, Cambridge and the UK, as well as less favored regions like Allentown, Pennsylvania, Youngstown, Ohio and places like that. Some of these locations are home to first-tier universities, some to universities that are in the second tier, and some don't have a university at all. We used a comparative methodology where possible choosing at least a couple of locations that were matched in the sense of being home to the same industry. And what we found was quite interesting. First, as we expected, the university role in these cases wasn't limited to the creation of new knowledge. Sometimes that was an important contribution, but we also saw several other roles. For example, they were playing an important role in attracting knowledge from elsewhere, most importantly in the form of people, students and faculty. They were unlocking knowledge that was tied up in existing firms and industries by helping to see the new possibilities that were obscured by old ways and traditional ways of doing things in those firms. They were helping to adapt knowledge from elsewhere to fit local circumstances, and they were helping to bring together to combine areas of activity, business activity, technical activity in the region that previously no one had recognized were connected. And also, by stepping back a little bit from the details of these 20 odd cases, we were able to identify four basic types of industrial transition in the regions that we studied. The first, the birth of a new industry with no antecedent in the region is the scenario that's most closely associated with the university role, that's what people think about when they think of Stanford or MIT and so on. The second is very different. It entails the importation of an industry into a region from somewhere else. And there are examples in our sample, the development of the electronics manufacturing industry in the Taipei Shinchu corridor in Taiwan and the automotive cluster that was established in upstate South Carolina and Southern North Carolina. In the third kind of transition, an industry in a region goes into decline, but its core technologies are redeployed and provide the basis for the emergence of a related new industry. And an example of that is the development of the advanced polymers industry, advanced polymer engineering sector in northeast Ohio, which grew out of the remnants of the failed tire industry in that part of the country. And finally, the fourth pathway involves upgrading of an existing industry in a region by infusing new technologies and enhancing the offerings of the product or services. And in our cases, that was the story of what happened in the NASCAR auto racing cluster in North Carolina, where over a period of decades, the application of advanced engineering tools and materials contributed to an enormous improvement in the performance of those NASCAR and also was associated with the emergence of a multi-billion dollar motorsports entertainment complex near Charlotte. And in a very different way, this was the story of the industrial machinery industry centered in temporary in Finland. Now in practice, the distinctions between these four types of transition aren't always as clear as this would suggest, but the taxonomy is useful because in each of our cases, one of these four categories did seem to dominate, and what also became clear is that the patterns of innovation associated with each type of transition are quite different. So for example, if we compare the indigenous creation of a new industry, that's the type one case, and the type four case of upgrading an existing industry, there are clear differences along most of the dimensions of the local innovation system, the kind of financing arrangements, the culture of innovation, the kind of anchor institutions, the most important kinds of education and training inputs and so on. And we also noticed that there are distinct patterns of engagement by higher education institutions in local economic development depending on which of these processes is taking place. So for example, for type one transitions involving the creation of a new science-based industry, the highest impact educational outputs of local universities are PhD level scientists and engineers with an interest in entrepreneurial careers and with some exposure to entrepreneurial business practices. And the other kinds of activities that are important here are mentorship programs for new businesses, proactive technology licensing programs, programs to broker ties between academic researchers and local entrepreneurs. For type four transitions involving the upgrading of existing industries, it looks quite different. The most important educational outputs are likely in this case to be bachelor's and master's level engineering graduates with knowledge of the industry's practices and problems. And other important things that universities do in this case are likely to include the development of industrially relevant degree and continuing education programs, internship and faculty leave opportunities in the local industry and technical problem solving through contract research and faculty consulting. For type two transitions involving the relocation of an industry or companies into a region, the key university activities in this case might include responding to the local manpower needs of the relocating firms, for example, by developing customized curricula and another important role maybe to provide technical assistance to local suppliers and subcontractors who are trying to engage with the incoming firms. And finally, for type three transitions involving diversification out of an old industry into a new one, a key role for the university in this case may be to cultivate links between disconnected players in the region, between startups and established firms, for example, or by establishing on campus forums for discussing new applications of local industrial technologies. If nothing else, all of this should cast doubt on the idea that there's a one-size-fits-all approach for the university role or the higher education institution role in economic development. What it suggests instead is that these institutions need to understand the pathways along which local industries are developing and what innovation processes are associated with them. They need to be able to say to themselves, this is the kind of region we're in, this is the kind of industry that we're engaging with, and because of that, this is what we at our education institution need to be doing. And the answer won't in general be the same for every institution. Indeed, the answer is likely to be different in different parts of the same university. So let me wind up here with a couple of closing points. First of all, I want to suggest that we need to shift in how we think about, make some shifts in how we think about this problem. We need to shift from thinking only about technology transfer from universities to thinking about technology take-up by industry, which is a very different thing. We need to shift from thinking only about universities as solving problems for industry to thinking about them also as public spaces for the identification of interesting new problems that might be solved. We need to shift from thinking only about universities as fountains of new knowledge to thinking about them also as forums bringing together otherwise disconnected local actors into contact with each other. And we also need to shift from thinking about local industries only as more or less self-contained clusters to thinking about them as hubs or nodes in global networks of knowledge and value creation. And finally, for takeaways I've said all of these things. First of all, innovation is about more than discovery and invention. It's about absorption and application of new knowledge which might not have been developed locally. In fact, in many cases it may be coming from elsewhere. Second, we need to avoid a one-size-fits-all approach to the development of local and regional innovation systems that different industries are going along different pathways and they need different kinds of participation by governments and by education institutions. Third, the standard model that we have come to think of of the economic role of the university is way too narrow. There are many ways in which universities contribute and we need a broader, more holistic and more differentiated view of this role in local innovation systems. And finally, building one of these systems is it takes time. It takes time and it takes patience and if universities are in it only for the money, they're going to fail. Thank you for your patience. I went a little bit longer than I had hoped but I hope that's not too long. Thank you.