Once upon a time, innovation was an isolationist sport. In America’s innovative economy 20 years ago, a worker drove to a nondescript office campus along a suburban corridor, worked in isolation, and kept ideas secret.

Today, by contrast and partly a result of the Great Recession, proximity is everything. Talented people want to work and live in urban places that are walkable, bike-able, connected by transit, and hyper-caffeinated. Major companies across multiple sectors are practicing “open innovation” and want to be close to other firms, research labs, and universities. Entrepreneurs want to start their companies in collaborative spaces, where they can share ideas and have efficient access to everything from legal advice to sophisticated lab equipment.

These disruptive forces are coming to ground in small, primarily urban enclaves—what we and others are calling “innovation districts.” By our definition, innovation districts cluster and connect leading-edge institutions with startups and spin-off companies, business incubators, and accelerators in the relentless pursuit of cutting-edge discoveries for the market. Compact, transit-accessible, and highly networked, they grow talent, foster open collaboration, and offer mixed-used housing, office, retail, and 21st century urban amenities. In many respects, the rise of innovation districts embodies the very essence of cities: an aggregation of talented, driven people assembled in close quarters, who exchange ideas and knowledge. It’s in the vein of what urban historian Sir Peter Hall calls “a dynamic process of innovation, imitation and improvement.”

Globally, Montreal, Seoul, Singapore, Medellin, Barcelona, Cambridge, and Berlin offer just a few examples of evolving innovation districts. In the US, the most iconic innovation districts can be found in the downtowns and midtowns of cities like Atlanta, Cambridge, Philadelphia, Pittsburgh, San Diego, and St. Louis, where advanced research universities, medical complexes, research institutions, and clusters of tech and creative firms are sparking business expansion, as well as residential and commercial growth. Even a cursory visit to Kendall Square in Cambridge, University City in Philadelphia, or midtown Atlanta shows the explosion of growth and mixed development occurring around institutions like MIT, the University of Pennsylvania, and Georgia Tech.

Other innovation districts can be found in Boston, Brooklyn, San Francisco, and Seattle, where former industrial and warehouse areas are charting a new innovative path, powered by their enviable location along transit lines, their proximity to downtowns and waterfronts, and their recent addition of advanced research institutions (reflected by Carnegie Mellon University’s decision to place its Integrative Media Program at the Brooklyn Navy Yard).

Perhaps the greatest validation of this shift is found in the efforts of traditional exurban science parks (like Research Triangle Park in Raleigh-Durham) to urbanize, in order to keep pace with the preferences of their workers for walkable communities and the preference of their firms to be near other firms and collaborative opportunities.

Innovation districts are already attracting an eclectic mix of firms in a diverse group of sectors, including life sciences, clean energy, design, and tech. We even see a return of small-scale and customized manufacturing, made possible by 3D printing, robotics, and other advanced techniques.

Unlike efforts to grow the “consumer city” via sports stadia, luxury housing, and high-end retail, innovation districts are intent on growing the firms, networks, and sectors that drive real, broad-based prosperity.

At a time of increasing concerns over inequality and resilience, innovation districts can spur productive, inclusive, and sustainable growth. If properly structured and scaled, they can provide a strong foundation for the commercialization of ideas, the expansion of firms, and the creation of jobs. They also offer the tantalizing prospect of expanding employment and educational opportunities for disadvantaged populations—many innovation districts are close to low- and moderate-income neighborhoods—as well as sparking more sustainable development patterns, given their embrace of transit, historic buildings, traditional street grids, and existing infrastructure.

Innovation districts represent one of the most positive trends that have emerged in the aftermath of the Great Recession. Smart cities, innovative companies, advanced universities, and financial institutions would be wise to embrace them.

This piece originally appeared on Quartz.

      

For years, corporate campuses like Silicon Valley were known for innovation. Located in suburban corridors that were only accessible by car, these places put little emphasis on creating communities where people work, live and go out.

But now, as the economy emerges from the recession, a shift is occurring where innovation is taking place. Districts of innovation can be found in urban centres as disparate as Montreal, Seoul, Singapore, Medellin, Barcelona, and London. They are popping up in the downtowns and midtowns of cities like Atlanta, Cambridge, Philadelphia, and St. Louis.

These are places where advanced research universities, medical complexes, and clusters of tech and creative firms are attracting businesses and residents.

Other innovation districts can be found in Boston, Brooklyn, San Francisco, and Seattle, where older industrial areas are being re-imagined and remade, leveraging their enviable location near waterfronts and city centres and along transit lines. Innovative companies and talented workers are flocking to these areas in abundance.

Even traditional science parks like Research Triangle Park in Raleigh-Durham are scrambling to urbanise to keep pace with their workers' preference for walkable communities and their companies' desire to be near other firms.

In these districts, leading anchor institutions and start-ups are clustering and connecting with one another. They are coming together with spin-off companies, incubators, and accelerators in the relentless pursuit of new discoveries for the market.

These areas are small and accessible, growing talent, fostering open collaboration, and offering housing and office space as well as modern urban amenities. They are both competitive places and "cool" spaces.

The growth of innovation districts is being driven by private and civic actors like universities, philanthropies, business associations and business improvement districts. Yet local governments play an important role in accelerating the growth of districts and maximising their potential . Three roles stand out:

1) Mayors are leading efforts to designate districts

Barcelona's former mayor Joan Clos set his eyes on transforming his city into a "city of knowledge". Through extensive, focused public planning and investment, Clos designed an innovation district from the debris of a 494-acre industrial area, which was scarred and separated from the rest of the city by railroad tracks. His vision included burying these tracks, increasing access via a new public tram, designing walkable streets, and creating new public spaces and housing.

Today, the area is a 21st-century urban community with 4,500 firms, thousands of new housing units, and clusters of universities, technology centres, and incubators.

Across the Atlantic in Boston, former mayor Tom Menino declared the South Boston waterfront an innovation district in 2010. Menino persuaded innovators like MassChallenge to move to the district and exacted important concessions from developers (including land for innovation-oriented retail, shared labs and other spaces, and micro-housing) to help realise the district's vision.

2) Changing land-use laws to build spaces with a mix of facilities

Barcelona and Research Triangle Park, for example, developed bold master plans encouraging the "mixing" of large and small firms, research facilities, housing, restaurants, and retail and outlining where to create open spaces for networking. Cambridge, Massachusetts, by contrast, has allowed incremental moves from rigid, antiquated rules to encourage similar outcomes in Kendall Square .

3) Supporting scarce public resources with large private and civic investments

In New York , former mayor Michael Bloomberg deployed $100m in municipal capital to prepare the infrastructure necessary to lure Cornell and Technion universities to Roosevelt Island. In other cities, including St Louis and Seattle, local resources are financing infrastructure improvements to buttress and accelerate private growth.

Given that many innovation districts are adjacent to low-income neighbourhoods, cities like Philadelphia are considering smart use of school investments to prepare disadvantaged youth for good jobs in the Stem (science, technology, engineering, and math) economy.

As this decade unfolds, we should expect more cities to use their powers in the service of this new model of innovative, inclusive, and resilient growth.

This opinion originally appeared in The Guardian

       

       

Event Information

View the report

The geography of innovation is shifting and a new model for innovative growth is emerging. In contrast to suburban corridors of isolated corporate campuses, innovation districts combine research institutions, innovative firms and business incubators with the benefits of urban living. These districts have the unique potential to spur productive, sustainable, and inclusive economic development.  

On June 9, the Metropolitan Policy Program at Brookings released “The Rise of Innovation Districts,” a new report analyzing this trend. The authors of the paper, Brookings Vice President Bruce Katz and Nonresident Senior Fellow Julie Wagner, were joined by leaders from emerging innovation districts across the country to discuss this shift and provide guidance to U.S. metro areas on ways to harness its potential.

Join the conversation on Twitter using #InnovationDistricts

Presentation by Bruce Katz

---

Event Photos

---

Bruce Katz, Vice President and Director, Metropolitan Policy Program

Lydia DePillis, John A. Fry, Nicole Fichera, Kofi Bonner, Julie Wagner

The Honorable Andy Berke, Mayor, City of Chattanooga, TN and Bruce Katz

Video

• What Are Innovation Districts?
• Innovation Districts Aligned With Disruptive Dynamics of Our Era
• Cities are For People
• Boston's District Hall a Living Room for Innovation Economy
• Innovation Happens Anywhere and Everywhere
• Are Innovation Districts Another Form of Gentrification?
• Philadelphia Behind but Competing in Innovation Districts
• Chattanooga Has Fastest, Cheapest Internet in Western Hemisphere
• We Live In an Innovation Century
• The Rise of Innovation Districts - Opening Remarks
• The Rise of Innovation Districts - Presentation by Bruce Katz
• The Rise of Innovation Districts - Panel Discussion
• The Rise of Innovation Districts - Moderated Dialogue

Audio

• The Rise of Innovation Districts: A New Geography of Innovation in America

Transcript

• Uncorrected Transcript (.pdf)

Event Materials

• 20140609_innovation_districts_transcript

       

The geography of innovation is shifting. For proof, start with Google, which over the past 10 years has taken the core R&D and innovation-oriented activities it once housed only in Silicon Valley and extended them into cities. The company’s presence in London’s Tech City, New York City’s Chelsea district, and Pittsburgh’s Bakery Square reflects management’s calculation that being in cities increases the company’s access to growing tech-oriented ecosystems, advanced research institutions, deep pools of talent, and distinct regional specializations.

In its decision to go urban, Google has been joined by not only other tech firms such as Twitter, Microsoft, and Spotify, but also companies like Comcast, Amazon, Pfizer, Quicken Loans, and countless numbers of small start-ups and entrepreneurs. (Our recent research for the Brookings Institution, “The Rise of Innovation Districts: A New Geography of Innovation in America,” provides the larger context for these corporate choices.)

For the past 50 years, the landscape of innovation has been dominated by regions like Silicon Valley—suburban corridors of spatially isolated corporate campuses, accessible only by car, with little emphasis on the quality of life or on integrating work, housing, and recreation. After visiting dozens of U.S. and European cities, interviewing hundreds of practitioners and experts on the ground, and scouring scholarly analyses of investor and firm behavior, we are convinced that a complementary new urban model is now emerging, in the form of what we and others are calling “innovation districts.”

These districts, by our definition, are “geographic areas where leading-edge anchor institutions and companies cluster and connect with start-ups, business incubators, and accelerators. Compact, transit-accessible, and technically-wired, innovation districts foster open collaboration, grow talent, and offer mixed-used housing, office, and retail.”

Globally, Barcelona, Berlin, Copenhagen, London, Medellin, Montreal, Seoul, Stockholm, and Toronto all contain emerging innovation districts. In the United States, the most iconic districts can be found in the downtowns and midtowns of Atlanta, Cambridge, Detroit, Philadelphia, Pittsburgh, and St. Louis. In each, advanced research universities, medical complexes, and clusters of tech and creative firms are sparking business expansion as well as residential and commercial growth.

Other innovation districts are developing in Boston, Brooklyn, Chicago, Portland, San Francisco, and Seattle. Former industrial and warehouse areas are undergoing a renaissance, powered by their enviable location along transit lines, proximity to downtowns and waterfronts, and recent additions of advanced institutions. (Note, for example, Carnegie Mellon University’s decision to place its Integrative Media Program at the Brooklyn Navy Yard.)

Perhaps the greatest validation of this shift is the fact that traditional exurban science parks like Research Triangle Park in Raleigh-Durham are now responding with efforts to meet the new demand for more vibrant and collaborative work and living environments.

Innovation districts are already attracting an eclectic mix of firms in the app economy and high tech sector as well as in high-value, research-oriented sectors such as life and material sciences, clean energy, and data computing. They are also home to companies in highly creative fields like architecture, design, theater production, advertising, and marketing. We even see a return to cities of small-scale and customized manufacturing, made possible by 3D printing, robotics, and other advanced techniques.

Much of this activity reflects a fundamental rethinking by corporate management about how and where innovation happens. In turn, it is making the case that discrete urban geographies can be instrumental in strengthening the competitive advantages of specific firms and clusters.

Rather than being the outgrowth of heavy-handed government programs, innovation districts are instead emerging from broader trends and market forces. For example, an economy increasingly oriented toward innovation (particularly through open collaborations) naturally rewards urban density. Companies, researchers, and entrepreneurs working in close proximity are able to share ideas rather than invent in isolation. No one company can master all the knowledge it needs, so they rely on a network of industry collaborators. A recent New York Times article on the growth of Pfizer, Novartis, and other major pharmaceutical companies in Cambridge, makes the point explicitly:

Pharmaceutical companies traditionally preferred suburban enclaves where they could protect their intellectual property in more secluded settings and meet their employees’ needs. But in recent years, as the costs of drug development have soared and R&D pipelines slowed, pharmaceutical companies have looked elsewhere for innovation. Much of that novelty is now coming from biotechnology firms and major research universities like MIT and Harvard, just two subway stops away.

If the benefits of urban density were already being experienced, they take on heightened importance in what Michael Mandel has called the “age of convergence” —when companies must simultaneously push forward with technology and content. Other analysis by the Center for an Urban Future in New York City finds many tech players focusing less on building new technologies and more on “applying technology to traditional industries like advertising, media, fashion, finance, and health care.” These shifts reinforce the importance of proximate location as companies strive to be physically close to the individuals and companies they partner with.

The rise of a convergence and collaborative economy also raises questions of how commercial buildings—offices, research labs, business incubators, and innovation institutes—should be designed. Thus, the creative solutions being tried in vanguard innovation districts will yield broad lessons. With their many variations on incubator space, collaborative venues, social networking, product competitions, technical support, and mentoring, they are beginning to sort out the best physical and social platforms for entrepreneurial growth.

Finally, large-scale demographic migrations are putting new value on cities and demanding more and better choices in where workers live, work and play. The City Observatory recently found, for example, that the number of young college graduates living within three miles of city centers (i.e., where innovation districts tend to be located) has surged, up 37 percent since 2000. This is happening not just in talent magnets like Denver, Portland, OR, and San Diego, but also in older industrial cities like Buffalo, Cleveland, and Pittsburgh.

The confluence of these disruptive economic, social, and demographic dynamics has changed corporate calculus. As companies design forward-looking strategies, they should be asking whether and how a greater commitment to urban locales could help them squeeze out even more success.

This commentary was originally published by Harvard Business Review.

       

In the year since we released “The Rise of Innovation Districts: A New Geography of Innovation in America,” Brookings has visited or interacted with dozens of leaders in burgeoning innovation districts in the United States and Europe. In so doing, we’ve sharpened our knowledge of what’s happening on the ground and gained some important insights into how cities and metros are embracing this new paradigm of economy-shaping, place-making, and network-building.

Innovation districts capture the remarkable spatial pattern underway in the innovation economy—the heightened clustering of anchor institutions, companies, and start-ups in small geographic areas of central cities across the United States, Europe, and other global-trading regions.

The rise of innovation districts has been situated against the familiar backdrop of suburban corporate campuses and science parks. Accessible only by car, these spatially isolated corridors place little emphasis on the quality of life or on integrating work, housing, and recreation.

By contrast, in our report we found the rise of urban innovation hubs to be the organic result of profound economic and demographic forces that are altering how we live and work. The growing application of “open innovation”—where companies work with other firms, inventors, and researchers to generate new ideas and bring them to market—has revalued proximity, density, and other attributes of cities. At the same time, the growing preference of young talented workers to congregate in vibrant neighborhoods that offer choices in housing, transportation, and amenities has made urban and urbanizing areas increasingly attractive.

We also found that innovation districts uniformly contain a mix of economic, physical, and networking assets. Economic assets are the firms, institutions, and organizations that drive, cultivate, or support an innovation-rich environment. Physical assets are the public and privately owned spaces—buildings, open spaces, streets, and other infrastructure—designed and organized to stimulate new and higher levels of connectivity, collaboration, and innovation. Lastly, networking assets are the relationships between actors—such as between individuals, firms, and institutions—that have the potential to generate, sharpen, and/or accelerate the advancement of ideas. These assets, taken together, create an innovation ecosystem—the synergistic relationship between people, firms, and place that facilitates idea generation and advances commercialization.

One year later, innovation districts continue to rise. What have we learned about how they are evolving?

First, the model of innovation districts has been embraced, co-opted, and (in some cases) misappropriated, further reinforcing the need for grounding this work in empirically based evidence.

A simple Google search will reveal the extent to which the language of “innovation districts” (or “innovation quarters,“ “innovation neighborhoods,” or “innovation corridors”) has rapidly permeated the field of urban and metropolitan economic development and place-making.

In some places, this labeling is being accurately used by globally recognized research institutions (e.g., Carnegie Mellon in Pittsburgh, Drexel University in Philadelphia) that are both experiencing extraordinary growth near their campuses as well as designing intentional efforts to build on their distinctive assets. In communities as diverse as Philadelphia, Pittsburgh, and St. Louis in the United States and Manchester and Sheffield in England, local leaders are conducting deep empirical analysis to understand their competitive advantages and existing weaknesses within their innovation ecosystem. They are exploring what it means to encourage greater collaboration and cooperation across their institutions, firms, and entrepreneurs. And they are exploring ways to better create “place” so as to increase overall vitality, facilitate innovation, and spur the growth of new businesses and jobs.

In other places, the nomenclature reflects an aspiration—and is spurring more deliberate efforts by local stakeholders to grow distinctive innovation ecosystems. In cities like Albuquerque, N.M., Chattanooga, Tenn., Chicago, Ill., Durham, N.C., and San Diego, Calif., local leaders are using the innovation district paradigm as a platform to measure their current conditions, develop strategies for addressing gaps and challenges, and build coalitions of stakeholders that can together help realize a unified vision for innovative growth. Some of these budding districts represent typologies not outlined in our report but that are ripe for future research, including “start-up” enclaves in or near downtowns of cities that lack a major anchor as well as “public markets” that blend locally produced food products and crafts with maker spaces, digital design, and other innovations in the creative arts.

There is one unfortunate trend in the rising use of the "innovation district" lexicon. In a number of cities, local stakeholders have applied the label to a project or area that lacks the minimum threshold of innovation-oriented firms, start-ups, institutions, or clusters needed to create an innovation ecosystem. This appears to result either from the chase to jump on the latest economic development bandwagon, the desire to drive up demand and real estate prices, or sometimes a true lack of understanding of what an innovation district actually is. The motivation for real estate developers to adopt the moniker seems clear: to achieve a price premium for their commercial, residential, and retail rents. Yet these sites are typically a collection of service-sector activities with little focus on the innovation economy. The lesson: labeling something innovative does not make it so.

From all these observations, it is clear that the field needs a routinized way to measure the starting assets of innovation districts—both to separate true districts from “in name only” ones as well as to give individual communities a platform for developing targeted strategies going forward. This means both running the numbers—conducting a quantitative audit—and undertaking a more qualitative assessment of strengths and weaknesses. Irrespective of their phase of development, innovation districts must evaluate the extent to which they have a critical mass of economic, physical, and networking assets to collectively generate the vitality that these districts demand. They need to evaluate the competitive advantages they have in certain economic sectors and learn how to cultivate them. And they need to ensure that they have the connectivity, diversity, and quality of place necessary to create a unique and vibrant environment in which innovation can thrive.

To facilitate this process, we are working in close collaboration with Mass Economics and the Project for Public Spaces to develop an audit template and tool. Over the next year, we intend to sharpen this tool in a subset of innovation districts across the country and then encourage others to employ it in their own established or burgeoning districts.

Second, the core economic assets of innovation districts are not fixed; in fact, many innovation districts are being created or enhanced by the relocation of major anchor facilities as institutions strive to achieve the highest return on investment.

The conventional notion of an “anchor” institution is that it is solidly weighted in a particular place. Yet over the past decade a substantial number of innovative companies and advanced educational and research institutions have moved key facilities and units as a means of generating greater innovation output. Examples of new locations include the University of California-San Francisco’s biotechnology campus in Mission Bay (2003); the University of Washington’s medical research hub in Seattle’s South Lake Union (2005); Brown University’s medical school in downtown Providence, R.I. (2011); Duke’s Clinical Research Institute in downtown Durham (2013); Carnegie Mellon University’s Integrative Media Program in the Brooklyn Navy Yard (2013); and, most famously, the new Cornell Tech campus on Roosevelt Island in New York City (2015).

These “first mover” relocations show how corporate and university leaders are departing from the tradition of building new facilities within their existing footprint and are willing to seek out new areas (and even new cities) to retain, or achieve, competitive advantage in their respective clusters and fields. As Cornell Professor Ronald Ehrenberg said about his school’s isolated Ithaca, N.Y. campus, “It is very, very difficult for us to do the kind of development through tech transfer that a place like Stanford or Berkeley can do in San Francisco or Harvard or MIT can do in Boston.” Our strong sense in talking with leaders around the country is that we are still at the early stage of corporate and university relocations given the extent to which urban areas have been revalued. The physical relocation of key innovation assets has now become a critical competitiveness strategy for companies, universities, and even states.

In some cases, the “unanchoring of anchors” is also compelling local leaders to rethink the traditional borders and boundaries of the innovation economy. In Philadelphia, for example, University City has always been recognized as a settled innovation hub, given the co-location of such anchor institutions as Drexel University, the University of Pennsylvania, the University City Science Center, and others. The recent decision of Comcast to consolidate its corporate presence in the downtown area and build its major new Innovation and Technology Center less than 10 blocks from 30th Street Station and the Drexel Campus is convincing some leaders to “stretch” Philadelphia’s University City district to incorporate this new corporate giant.

Third, almost all innovation districts have significant work ahead to understand the rising value of “place” in the innovation ecosystem and leverage or reconfigure their physical assets to create dense and dynamic communities.

While our paper dissected various types of physical assets to help practitioners understand their individual roles and value, the more important message to convey now is the imperative to combine and activate physical assets in ways that create vibrant “places.” The Project for Public Spaces aptly describes place as “…environments in which people have invested meaning over time. A place has its own history—a unique cultural and social identity that is defined by the way it is used and the people who use it.”¹

Our review of innovation districts, including those cited in our paper, reveals that many have not yet maximized the potential for creating lively communities in which their residents and workers feel invested, reducing the potential innovation output of these communities. When designed and programmed well, a district’s public spaces—whether within buildings or outside of them—facilitate open innovation by offering numerous opportunities to meet, network, and brainstorm. Strong places entice residents and workers to remain in the area off hours, extending the opportunities for collaboration. Strong places create a culturally and educationally enriched environment that strengthens human interaction, knowledge, and motivation.

While some university-led districts have made some improvements over the years, districts anchored by medical campuses have significant work ahead. These spaces were designed as isolated fortresses that valued parking over walking (ironic given their health mission), with little or no attention paid to amenities, cultural activities, retail, or housing. Significantly, some medical campuses are often located in close proximity to downtowns, as part of universities, or near organic entrepreneurial communities (e.g., the proximity of Oklahoma City’s Health District to Automobile Alley). This raises the potential for smart (and related) place-making activities in a nearby area and reinforces the need to rethink traditional geographies and artificial boundaries when considering interventions.

Fourth, the rapid growth and impact of national intermediaries (what we call innovation cultivators) shows real promise in helping innovation districts grow and steward their networking assets and stimulating new innovation opportunities.

The past year has seen substantial growth in multicity intermediaries along with scores of locally grown accelerators and incubators. It appears more than ever that intermediaries are increasingly the catalyst to growing innovation and entrepreneurial energy within local districts and across start-ups, small and medium-sized enterprises, and, even to some extent, large companies and research institutions. They are designed to think and act horizontally, encouraging people and firms to interact and work together in ways and at a scale previously unseen.

A growing and increasingly important role for intermediaries is helping innovation districts evolve from the traditional “research and development” model to a “search and development” one, where crucial answers to their innovation questions and technological challenges are discovered by finding and collaborating with other firms. Some districts immediately recognized this potential and have gone to great lengths to grow, lure, and fund the development of multiple intermediaries across their districts.

The Cortex Innovation Community in St. Louis has, in a short period, clustered new buildings owned and/or supported by a number of well-respected intermediaries. These development and programmatic moves are effectively creating a new focal point for Cortex innovation activities. The new Cambridge Innovation Center, which offers space for start-ups combined with access to venture capital firms, professional services, and a plug-and-play physical environment, is already at 85 percent occupancy. A newly constructed Tech Shop—a do-it-yourself “maker space” equipped with industrial tools, machinery, and technology to support entrepreneurs—is under construction nearby. The near complete renovation of the Center for Emerging Technologies, which provides training, specialized facilities, and technical support, adds yet another layer of support for entrepreneurs and start-ups. Adding more to this mix is a soon-to-be-constructed space for tech-commercial activities combined with new housing, which will exponentially increase the number of people in a very small radius.²

As one can imagine, this clustering was deeply intentional and viewed as a way to stimulate new relationships, new networks, and the cross-fertilization of ideas; Cortex refers to this deliberate process as “innovation engineering.” We anticipate more innovation districts to follow suit, pursuing, if not cultivating, such intermediaries in their own innovation ecosystems.

Finally, the rise of innovation districts takes place in a national and urban political environment that demands inclusive growth and equitable outcomes.

The past year has seen the elevation of income inequality and social mobility as issues of national and urban significance. With the federal government mired in partisan gridlock, cities have become the vanguard of efforts to raise the minimum wage, expand affordable housing, and extend pre-K education, among other initiatives. These efforts come at a time when the civil unrest in Baltimore and Ferguson has refocused national attention on neighborhoods of high poverty.

Because of their location in the cores of central cities, many established and emerging innovation districts are located several blocks away from distressed communities. This proximity creates an enormous opportunity to show the positive impact that innovative growth can have on inclusive outcomes. Innovation districts create employment opportunities that can be filled by local residents and procurement and construction opportunities that can be fulfilled by local vendors and contractors. The districts generate tax revenues that can be used to fund neighborhood services and neighborhood regeneration. And they offer the potential to link the ample expertise and talent in anchor educational institutions with the needs of neighborhood schools and children.

Recognizing these benefits, local leaders are demonstrating a genuine commitment to growing more inclusive districts. In our work, we’ve seen several early models that could be built on and replicated. In the Barcelona 22@ district, for example, leaders are trying to quantify the growth in service jobs accessible to local and regional residents while, at the same time, connecting those residents to training that increases their skills in more innovation-oriented sectors. Last year, Drexel University opened a new “urban extension center” that offers career-building workshops, legal clinics, and other services to residents of the adjacent Mantua Promise Zone. The Evergreen Cooperative in Cleveland’s University Circle district has been working for several years to leverage local purchasing power to create business ownership and employment opportunities for low-income residents. And in Baltimore, the University of Maryland partnered with surrounding neighborhood organizations, residents, and institutions to develop a detailed new plan for building what the Baltimore Southwest Partnership envisions as a “diverse, cohesive community of choice built on mutual respect and shared responsibility.”

These examples represent concrete initiatives to ensure that nearby neighborhoods and their residents connect to and benefit from new growth opportunities in innovation districts and beyond. Scaling such efforts will be critical in the years to come, as the success of these districts will be defined in large part by their broader city and regional impacts.

As Brookings works this year to help unleash more innovation districts across the U.S. and Europe, we will continue to hone our observations and knowledge about trends, challenges, and strategies. We will compile and publish what we have learned for anchor leaders, policymakers, scholars, and practitioners, focusing on many of the issues—accelerating commercialization to improving inclusion—noted above. We will do this work in close collaboration with proven organizations like Mass Economics and Project for Public Spaces. We look forward to contributing to this rapidly changing space via empirical and on-the-ground research, strategy and policy development, convenings, and network building. Stay tuned.

Read The Rise of Innovation Districts: A New Geography of Innovation in America

---

       

Less than two years ago, the Brookings Institution unveiled the research paper, “The Rise of Innovation Districts,” which identified an emerging spatial pattern in today’s innovation economy. Marked by a heightened clustering of anchor institutions, companies, and start-ups, innovation districts are emerging in central cities throughout the world.

A Google search of the term “innovation district” reveals over 200,000 results, indicating the extent to which the phrase has permeated the fields of urban economic development, planning, and placemaking. The term is used to refer to areas, often in the downtowns of cities, where R&D-laden universities or firms are surrounded by a growing mix of start-ups and spin-offs. The term is also increasingly applied to densely populated urban neighborhoods where firms like Google are establishing campuses. But it also pops up to describe new office complexes whose amenities include a few stores or a fashionable coffee shop.

The variation in understanding of the term and its application suggests the need for a routinized way to measure the essential quantitative and qualitative assets of innovation districts. Given this, for the past nine months the Brookings Institution, Project for Public Spaces (PPS), and Mass Economics have collaborated to devise and test an audit tool for assessing innovation districts.

What to count? Considerations in designing an audit

Innovation ecosystems comprise complex, overlapping relationships between firms, individuals, unique spaces, private real estate, public infrastructure, capital, expertise, and conviviality, congregated in a roughly delineated area. To begin to determine how to identify and measure assets, we developed a process that was both rigorous and reflective, drawing together some of the brightest minds in the field, top practitioners on the ground, and a team strong in quantitative analysis.

First, we conducted research across numerous relevant topics including entrepreneurship, real estate development, commercialization, economic geography, city planning, institutional culture, finance, and inclusive development. This exercise generated hundreds of potentially applicable measures for the audit.  

       

Innovative activity and innovation districts are not evenly distributed across cities. Some metropolitan areas may have two or three districts scattered about, while other cities are lucky to have the critical mass to support even one strong district. London, however, a global city with nearly unparalleled assets, can best be understood as not just a collection of innovation districts but as a contiguous “city of innovation.” 

Our understanding of that innovative activity has taken a leap forward with the publication of a new report by the Centre for London called "Spaces to Think". Even for a paragon of innovation, a critique such as this is imperative if the city desires to maximize its assets while continuing to grow in a sustainable and inclusive manner. Much as we have recommended that urban leaders across the United States undertake an asset audit of their districts to identify key priorities, "Spaces to Think" focuses on 17 distinct districts, mapping their assets, classifying their typologies, and identifying governance structures.

The 17 study areas in "Spaces to Think"

The report provides lessons applicable to many cities.

Having identified, across all 17 districts, the three major drivers of innovative activity—talent, space, and financing—it becomes clear that the main hurdle for London, as a global magnet of talent and capital, is affordable physical space: “Increasing pressure for land…risks constraining London’s potential as a leading global city for innovation.” Similar to hot-market cities across the United States, many of the study areas of greatest promise are older industrial areas, such as Here East, Canary Wharf, and Kings Cross, where large plots of underutilized land have been reimagined as innovation districts. 

But who is prepared to undertake new regeneration projects? The report places significant responsibility on London’s many universities—whose expansions already account for much of the large-scale development opportunities in the city—for a “third mission” of local economic development. It is universities, the report notes, that are “devoting increasing amounts of money, resources, and planning to building new or redesigned facilities…pitched as part of a wider regeneration strategy, or the creation of an innovation district.” 

A second concern is the democratization of the innovation economy. Already a victim of rising inequality, London’s future growth must reach down the ladder. As we’ve argued, with intentionality and purpose, innovation districts can advance a more inclusive knowledge economy, especially given that they are often abut neighborhoods of above-average poverty and unemployment. Spaces to Think expands upon four key strategies: local hiring and sourcing practices for innovation institutions; upskilling of local residents through vocational and technical programs within local firms; increased tax yield, especially given recent reforms in which “local authorities retain 100 percent of business rates”; and shared assets and rejuvenation of place. This final lever requires inclusive governance that encourages neighborhood ownership of the public realm.

Finally, the report notes that, while there is much diversity of leadership in the study areas—some are university-led, some are entrepreneurial, some are industry-led—“good governance and good relations between institutions, are at the heart of what makes innovation districts tick.” This issue is at the heart of our work moving forward: identifying and spreading effective governance models that encourage collaboration and coordination between the public, private, and civic actors within innovation districts.

We are pleased that this future work will be strengthened by a new partnership between the Bass Initiative on Innovation and Placemaking and the Centre for London. The ambition of this Transatlantic Innovation Districts Partnership is to increase our mutual understanding of innovation districts found in Europe through additional qualitative and quantitative analysis and to integrate European leaders into a global network, all to accelerate the transfer of lessons and best practices from districts across the world.

       

A Q&A with Dennis Lower, president and CEO, Cortex Innovation Community

As leaders scan the landscape for strong examples of innovation districts, their tour is hardly complete without learning of the Cortex Innovation Community—an innovation district in the heart of St. Louis. We sat down with Dennis Lower, president and CEO of the Cortex Innovation Community to learn what kinds of interventions and instruments are driving their success.

What is the Cortex Innovation Community?

Cortex is the region’s largest innovation hub, generating 3,800 tech-related jobs and over $500 million in investment in the last 14 years. It’s located close to downtown and built on the intellectual assets and resources of St. Louis’ leading universities, a premier health care provider, and the Missouri Botanical Garden. The focal point is the 200 acres of old industrial land that one time separated these institutions but that now stitches them together. At full build-out, Cortex will likely generate $2 billion of development and create 13,000 jobs.

What sets Cortex apart from other innovation districts?

Of course, every district is distinctive and unique, building off its local character, culture, and assets. What sets Cortex apart, I would argue, is that we literally have billions of dollars of academic, cultural, and recreational assets in the neighborhoods that surround the district, which other places simply do not have.

We are bookended by two universities—Washington University and St. Louis University—each a magnet for international students and each with a reputation for research and academic excellence. Washington University, for example, was one of five consortium members funded by the National Institutes of Health to map the human genome. These universities, together with the University of Missouri-St. Louis, are the academic bedrock of our local innovation ecosystem.

Recent demographic analysis tells us we are now the most diverse employment environment in the region no matter how you slice it, including by age, ethnicity, and educational attainment.

Another Cortex advantage is the neighborhood that surrounds us. In addition to historic housing, the Grand Center arts district is to the east, to the west is Forest Park, which contains the St. Louis Zoo, fine arts and history museums, two golf courses, the St. Louis Science Center, abundant walking and biking trails, and the internationally renowned Botanical Garden. Restaurant corridors are to the north and south. I tell you all this to say that Cortex is where innovation, tech, culture, and community collide—and people are hungry for this mix.

Cortex Innovation Community is also a tax-exempt 501(c)3 that oversees the design and development of the innovation district. What makes your nonprofit unique in managing this district?

Cortex has been designated the master developer to transform an old industrial district into a center for innovation and commercialization. We are in a particularly advantageous position because the state and the city have granted the 501(c)3 powers of eminent domain, the power to abate taxes, and the power to approve or reject building plans. From a traditional economic development perspective, these powers have been critical in overcoming obstacles that land speculators sometimes put in our way. We have not had to use this power very often, fortunately. Only a handful of properties were acquired under the threat of eminent domain, and we reached an impasse only twice, sending us to court to purchase those properties. We take this responsibility seriously and only use eminent domain powers sparingly. We have a good reputation with the public as a result.

Can you describe one accomplishment you are particularly proud of?

We knew that to jump-start an innovation district it was essential to build entrepreneurial density. We developed an unorthodox strategy of sorts in that we built a concentration of innovation assets all within a block of each other. Today, we have six innovation centers, each with its own community and programming: the Center for Emerging Technologies, a traditional technical assistance incubator for information technology, bioscience, and consumer/manufacturing products; the BioGenerator, an accelerator with shared wet lab space and $3 million of shared core lab equipment; TechShop, a premier maker work space for prototyping and creating; the Cambridge Innovation Center–St. Louis, a co-working office and lab startup space); Venture Café–St. Louis, a shared public space for the startup community to meet weekly with 8 to 12 unconventional breakout educational sessions; and IdeaLabs/MedLaunch, a unique university graduate/undergraduate incubator that develops new technology to solve clinical problems. This strategy is working beyond our wildest expectations. It’s the “secret sauce” for supercharging our district’s innovation ecosystem. 

Venture Café: one of the six innovation centers that weekly draws together over 500 entrepreneurs from all technology sectors.

Can you highlight one particularly interesting innovation or invention coming out of Cortex? 

Let me highlight two. We have over 200 companies in Cortex—there’s too much innovation happening here to highlight only one!

First, we have a medical device company that is changing the way infectious diseases are diagnosed. Its products can rapidly detect bacterial infections, determine if the infection is resistant to a range of antibiotics, and provide clinicians with patient-specific guidance to treat infections quickly and accurately. Their first product can diagnose urinary tract infections in just three hours.

And then we have a company tackling the biggest challenge in agriculture today—preventing insects, diseases, and weeds from destroying food crops. This company is developing a cost-effective technology to produce and topically deliver RNAi for agricultural crops. Put simply, this technology helps plants develop desired genetic traits without the use of genetically modified organisms, or GMOs. This could be transformative.

Many people have asked us how innovation districts are supporting inclusive growth. There is a concern that innovation districts are focusing on innovation to the exclusion of employment of city residents, who may not possess the skills or education the district’s businesses are seeking.

We look at inclusion as an integral part of our work and mission at Cortex. We currently have six inclusion initiatives and will soon introduce two more. One of those is the development of a magnet high school in the St. Louis Public School District, the Collegiate School for Medicine and Biosciences. Working closely with the school district’s superintendent and an important group of institutional and civic leaders, we have been developing an urban high school centered on one of the major strengths of our Cortex sponsors—bioscience. We recruited our first class in 2013, providing instruction in a small, temporary school, and in 2015 moved to a permanent location that can support 400 students.

The students come from all across the region, representing the largest spread of zip codes of any regional public school. Currently, 53 percent of the students are African American, 23 percent are Asian, and 22 percent are white, representing a great mix. Last year’s proficiency testing in math and English revealed that we ranked first across the entire public school system. I find this particularly gratifying because a number of incoming freshmen were not performing at grade level. What this tells us is given the opportunity, creative teaching approaches, and a supportive structure, these kids will excel quickly. With our incoming 9th grade class this August, we will have a full complement of freshmen to seniors, graduating our first class in 2017.

Perhaps one of these students will find the next cure for cancer. To me, this illustrates an important part of our district’s DNA—to grow and cultivate innovation talent for the future.

       

The tide of uncertainty that has swept the United Kingdom after its vote to leave the European Union has spared few—including its emerging class of innovation districts.

These hubs of innovation—where anchor institutions, such as universities and R&D laden companies cluster and connect with startups, incubators, and a host of public spaces, coffee shops, retail and housing—are now asking themselves important questions that will affect their future. Will the U.K. broker a deal to continue free trade with Europe? Will access to talent across Europe be curtailed? Will the devalued pound keep U.K. advanced manufacturers competitive for the medium to long term? Will European Union legal frameworks be replaced with a regulatory platform that continues to support advanced sectors? What will happen to EU funding on science and innovation, such as Horizon 2020?

Of course, innovation districts are no stranger to uncertainty, if not chaos. These districts thrive on random mixing, on smashing different kinds of disciplines and people together to generate new ideas and new products for the market. In this close-knit, highly networked ecosystem, chaos breeds creativity. At the same time, the backbone of districts is a clear regulatory and legal framework with rules on intellectual property, investment, and funding streams. The twinning of chaos and certainty is what makes these places simply superb spaces to incubate new technology, aggregate talent, and experiment in linking placemaking with innovation.

Yet from the distinctive innovation districts in London to those emerging in the middle of England, such as in Sheffield and Manchester, to those rising in Scotland, such as in Glasgow, this moment of uncertainty could be not only painful—it could be downright dangerous. 

In the face of such uncertain times, the temptation will be to sit back and wait for the cards to fall. But this tempered, conservative approach is ironically the more risky tactic.

We recommend another path.

Now is the time for the institutions and firms that are driving innovation districts to strengthen their competitive position and expand their reach.

Now is the time to try new forms of collaboration between universities, large companies, and local enterprises.

Now is the time to test more democratic modes of innovation with maker spaces, fab labs, and shared infrastructure and equipment.

Now is the time to forge new partnerships with other innovation districts in the United States and Europe to share promising strategies around commercialization, networking, and financing.

Now is the time to apply new energy to creative placemaking, including strengthening the innovation–place nexus around key nodes and applying quick interventions around traffic calming, bike lanes, and pop-up gathering spaces. 

U.S. cities and innovation districts have demonstrated that progress can persist even when higher levels of government are adrift. U.K. cities and districts can do the same.

       

How are two innovation districts in Stockholm successfully melding their tech and life science clusters to create new products?

What can the Wake Forest Innovation Quarter in North Carolina teach us about creating strong, vibrant, and innovative places?

How are innovation districts in Australia leveraging government policies and programs to accelerate their development?

Over the last year, members of the Anne T. and Robert M. Bass Initiative on Innovation and Placemaking team talked with hundreds of local leaders and practitioners advancing innovation districts in almost every global region. These conversations revealed the remarkable level of creativity and innovative, out-of-the-box thinking being employed to grow individual innovation districts.

In the course of our work, we have been intrigued by the question, is there value to be gained from a global network of innovation districts? To this end, we have reached out to successful global networks in Europe, the United States, and Asia to distill what it takes to make a strong and sustainable global network. Among our findings so far:

• Network members are solving on-the-ground challenges by talking with and learning from their peers. Several said that these horizontal exchanges are essential to leapfrogging ahead. 

• Online interaction is growing but network members say that face-to-face contact is critical. Comparing notes, asking questions, and engaging in conversations foster collaboration while maintaining a healthy dose of competition. 

• The right tools and supports can make all the difference. In networks where participants had full schedules, developing new ways to share intelligence, like early morning webinars or virtual conferences, regular e-newsletters, and simple methods to share data helped facilitate their learning. 

To what extent do you feel that a network of innovation districts might supercharge your own efforts and successes?

It would help our work tremendously if you could complete our on-line survey. It will take two minutes or less!

Editor's Note: If you're interested in receiving the latest news from the Bass Initiative, please sign up for our newsletter at this link, http://connect.brookings.edu/bass-initiative-newsletter-signup. Feel free to share it widely.

       

       

       

       

       

       

ABSTRACT

Recent literature has expressed considerable pessimism about the prospects for both productivity and overall economic growth in the U.S. economy, based either on the idea that the pace of innovation has slowed or on concern that innovation today is hurting job creation. While recognizing the problems facing the economy, this paper offers a more optimistic view of both innovation and future growth, a potential return to the innovation and employment-led growth of the 1990s. Technological opportunities remain strong in advanced manufacturing and the energy revolution will spur new investment, not only in energy extraction, but also in the transportation sector and in energy-intensive manufacturing. Education, health care, infrastructure (construction) and government are large sectors of the economy that have lagged behind in productivity growth historically. This is not because of a lack of opportunities for innovation and change but because of a lack of incentives for change and institutional rigidity.

Download the full paper »

Downloads

• U.S. Productivity Growth: An Optimistic Perspective

       

The weakness of the economic recovery in advanced economies raises questions about the ability of new technologies to drive growth. After all, in the years since the global financial crisis, consumers in advanced economies have adopted new technologies such as mobile Internet services, and companies have invested in big data and cloud computing. More than 1 billion smartphones have been sold around the world, making it one of the most rapidly adopted technologies ever. Yet nations such as the United States that lead the world in technology adoption are seeing only middling GDP growth and continue to struggle with high unemployment.

There are many reasons for the restrained expansion, not least of which is the severity of the recession, which wiped out trillions of dollars of wealth and more than 7 million US jobs. Relatively weak consumer demand since the end of the recession in 2009 has restrained hiring and there are also structural issues at play, including a growing mismatch between the increasingly technical needs of employers and the skills available in the labor force. And technology itself plays a role: companies continue to invest in labor-saving technologies that reduce demand for less-skilled workers.

So are we witnessing a failure of technology? Our answer is "no." Over the longer term, in fact, we see that technology continues to drive productivity and growth, a pattern that has been evident since the Industrial Revolution; steam power, mass-produced steel, and electricity drove successive waves of growth, which has continued into the 21st century with semiconductors and the Internet. Today, we see a dozen rapidly-evolving technology areas that have the potential for economic disruption as well in the next decade. They fall into four groups: IT and how we use it; machines that work for us; energy; and the building blocks of everything (next-gen genomics and synthetic biology).

Wide ranging impacts

These disruptive technologies not only have potential for economic impact—hundreds of billions per year and even trillions for the applications we have sized—but also are broad-based (affecting many people and industries) and have transformative effects: they can alter the status quo and create opportunities for new competitors.

While these technologies will contribute to productivity and growth, we must look at economic impact in a broader sense, which includes measures of surplus created and value shifted (for instance from producers to consumers, which has been a common result of Internet adoption). The greatest benefit we measured for autonomous vehicles—cars and trucks that can proceed from point A to point B with little or no human intervention. The largest economic impact we sized for autonomous vehicles is the enormous benefit to consumers that may be possible by reducing accidents caused by human error by 70 to 90 percent. That could translate into hundreds of billions a year in economic value by 2025.

Predicting how quickly even the most disruptive technologies will affect productivity is difficult. When the first commercial microprocessor appeared there was no such thing as a microcomputer—marketers at Intel thought traffic signal controllers might be a leading application for their chip. Today we see that social technologies, which have changed how people interact with friends and family and have provided new ways for marketers to connect with consumers, may have a much larger impact as a way to raise productivity in organizations by improving communication, knowledge-sharing, and collaboration.

There are also lags and displacements as new technologies are adopted and their effects on productivity are felt. Over the next decade, advances in robotics may make it possible to automate assembly jobs that require more dexterity than machines have provided or are assumed to be more economical to carry out with low-cost labor. Advances in artificial intelligence, big data, and user interfaces (e.g., computers that can interpret ordinary speech) make it possible to automate many knowledge worker tasks.

More good than bad

There are clearly challenges for societies and economies as disruptive technologies take hold, but the long-term effects, we believe, will continue to be higher productivity and growth across sectors and nations. In earlier work, for example, we looked at the relationship between productivity and employment, which are generally believed to be in conflict (i.e., when productivity rises, employment falls). And clearly, in the short term this can happen as employers find that they can substitute machinery for labor—especially if other innovations in the economy do not create demand for labor in other areas. However, if you look at the data for productivity and employment for longer periods—over decades, for example—you see that productivity and job growth do rise in tandem.

This does not mean that labor-saving technologies do not cause dislocations, but they also eventually create new opportunities. For example, the development of highly flexible and adaptable robots will require skilled workers on the shop floor who can program these machines and work out new routines as requirements change. And the same types of tools that can be used to automate knowledge worker tasks such as finding information can also be used to augment the powers of knowledge workers, potentially creating new types of jobs.

Over the next decade it will become clearer how these technologies will be used to raise productivity and growth. There will be surprises along the way—when mass-produced steel became practical in the 19th century nobody could predict how it would enable the automobile industry in the 20th. And there will be societal challenges that policy makers will need to address, for example by making sure that educational systems keep up with the demands of the new technologies.

For business leaders the emergence of disruptive technologies can open up great new possibilities and can also lead to new threats—disruptive technologies have a habit of creating new competitors and undermining old business models. Incumbents will want to ensure their organizations continue to look forward and think long-term. Leaders themselves will need to know how technologies work and see to it that tech- and IT-savvy employees are included in every function and every team. Businesses and other institutions will need new skill sets and cannot assume that the talent they need will be available in the labor market.

       

American voters are angry. But while the ill effects of globalization top their list of grievances, nobody is well served when complex economic issues are reduced to bumper-sticker slogans – as they have been thus far in the presidential campaign.

It is unfair to dismiss concerns about globalization as unfounded. America deserves to have an honest debate about its effects. In order to yield constructive solutions, however, all sides will need to concede some inconvenient truths – and to recognize that globalization is not the same phenomenon it was 20 years ago.

Protectionists fail to see how the United States’ eroding industrial base is compatible with the principle that globalization boosts growth. But the evidence supporting that principle is too substantial to ignore.

Recent research by the McKinsey Global Institute (MGI) echoes the findings of other academics: global flows of goods, foreign direct investment, and data have increased global GDP by roughly 10% compared to what it would have been had those flows never occurred. The extra value provided by globalization amounted to $7.8 trillion in 2014 alone.

And yet, the shuttered factories dotting America’s Midwestern “Rust Belt” are real. Even as globalization generates aggregate growth, it produces winners and losers. Exposing local industries to international competition spurs efficiency and innovation, but the resulting creative destruction exacts a substantial toll on families and communities.

Economists and policymakers alike are guilty of glossing over these distributional consequences. Countries that engage in free trade will find new channels for growth in the long run, the thinking goes, and workers who lose their jobs in one industry will find employment in another.

In the real world, however, this process is messy and protracted. Workers in a shrinking industry may need entirely new skills to find jobs in other sectors, and they may have to pack up their families and pull up deep roots to pursue these opportunities. It has taken a popular backlash against free trade for policymakers and the media to acknowledge the extent of this disruption.

That backlash should not have come as a surprise. Traditional labor-market policies and training systems have not been equal to the task of dealing with the large-scale changes caused by the twin forces of globalization and automation. The US needs concrete proposals for supporting workers caught up in structural transitions – and a willingness to consider fresh approaches, such as wage insurance.

Contrary to campaign rhetoric, simple protectionism would harm consumers. A recent study by the US President’s Council of Economic Advisers found that middle-class Americans gain more than a quarter of their purchasing power from trade. In any event, imposing tariffs on foreign goods will not bring back lost manufacturing jobs.

It is time to change the parameters of the debate and recognize that globalization has become an entirely different animal: The global goods trade has flattened for a variety of reasons, including plummeting commodity prices, sluggishness in many major economies, and a trend toward producing goods closer to the point of consumption. Cross-border flows of data, by contrast, have grown by a factor of 45 during the past decade, and now generate a greater economic impact than flows of traditional manufactured goods.

Digitization is changing everything: the nature of the goods changing hands, the universe of potential suppliers and customers, the method of delivery, and the capital and scale required to operate globally. It also means that globalization is no longer exclusively the domain of Fortune 500 firms.

Companies interacting with their foreign operations, suppliers, and customers account for a large and growing share of global Internet traffic. Already half of the world’s traded services are digitized, and 12% of the global goods trade is conducted via international e-commerce. E-commerce marketplaces such as Alibaba, Amazon, and eBay are turning millions of small enterprises into exporters. This remains an enormous untapped opportunity for the US, where fewer than 1% of companies export– a far lower share than in any other advanced economy.

Despite all the anti-trade rhetoric, it is crucial that Americans bear in mind that most of the world’s customers are overseas. Fast-growing emerging economies will be the biggest sources of consumption growth in the years ahead.

This would be the worst possible moment to erect barriers. The new digital landscape is still taking shape, and countries have an opportunity to redefine their comparative advantages. The US may have lost out as the world chased low labor costs; but it operates from a position of strength in a world defined by digital globalization.

There is real value in the seamless movement of innovation, information, goods, services, and – yes – people. As the US struggles to jump-start its economy, it cannot afford to seal itself off from an important source of growth.

US policymakers must take a nuanced, clear-eyed view of globalization, one that addresses its downsides more effectively, not only when it comes to lost jobs at home, but also when it comes to its trading partners’ labor and environmental standards. Above all, the US needs to stop retrying the past – and start focusing on how it can compete in the next era of globalization.

Editor's note: this piece first appeared on Project-Syndicate.org.

       

November's U.S. Bureau of Labor Statistics (BLS) employment report showed continued improvement in the job market, with employers adding 211,000 workers to their payrolls and hourly pay edging up compared with its level a year ago. The pace of job growth was similar to that over the past year and somewhat slower than the pace in 2014. For the 69^th consecutive month, private-sector payrolls increased. Since the economic recovery began in the third quarter of 2009, all the nation’s employment gains have occurred as a result of expansion in private-sector payrolls. Government employment has shrunk by more than half a million workers, or about 2.5 percent. In the past twelve months, however, public payrolls edged up by 93,000.

The good news on employment gains in November was sweetened by revised estimates of job gains in the previous two months. Revisions added 8,000 to estimated job growth in September and 27,000 to job gains in October. The BLS now estimates that payrolls increased 298,000 in October, a big rebound compared with the more modest gains in August and September, when payrolls grew an average of about 150,000 a month.

Average hourly pay in November was 2.3 percent higher than its level 12 months earlier. This is a slightly faster rate of improvement compared with the gains we saw between 2010 and 2014. A tighter job market may mean that employers are now facing modestly higher pressure to boost employee compensation. The exceptionally low level of consumer price inflation means that the slow rate of nominal wage growth translates into a healthy rate of real wage improvement. The latest BLS numbers show that real weekly and hourly earnings in October were 2.4 percent above their levels one year earlier. Not only have employers added more than 2.6 million workers to their payrolls over the past year, the purchasing power of workers' earnings have been boosted by the slightly faster pace of wage gain and falling prices for oil and other commodities.

The BLS household survey also shows robust job gains last month. Employment rose 244,000 in November, following a jump of 320,000 in October. More than 270,000 adults entered the labor force in November, so the number of unemployed increased slightly, leaving the unemployment rate unchanged at 5.0 percent. In view of the low level of the jobless rate, the median duration of unemployment spells remains surprisingly long, 10.8 weeks. Between 1967 and the onset of the Great Recession, the median duration of unemployment was 10.8 weeks or higher in just seven months. Since the middle of the Great Recession, the median duration of unemployment has been 10.8 weeks or longer for 82 consecutive months. The reason, of course, is that many of the unemployed have been looking for work for a long time. More than one-quarter of the unemployed—slightly more than two million job seekers—have been jobless for at least 6 months.  That number has been dropping for more than five years, but remains high relative to our experience before the Great Recession.

If there is bad news in the latest employment report, it's the sluggish response of labor force participation to a brighter job picture. The participation rate of Americans 16 and older edged up 0.1 point in November but still remains 3.5 percentage points below its level before the Great Recession. About half the decline can be explained by an aging adult population, but a sizeable part of the decline remains unexplained. The participation rate of men and women between 25 and 54 years old is now 80.8 percent, exactly the same level it was a year ago but 2.2 points lower than it was before the Great Recession. Despite the fact that real wages are higher and job finding is now easier than was the case earlier in the recovery, the prime-age labor force participation rate remains stuck well below its level before the recession. How strong must the recovery be before prime-age adults are induced to come back into the work force? Even though the recovery is now 6 and a half years old, we still do not know.

      

Editor’s note: The following remarks were prepared and delivered by Gary Burtless at a roundtable sponsored by the American Tax Policy Institute on January 7, 2016. Video of Burtless’ remarks are also available on the Institute’s website. Download the related slides at the right. 

We are here to discuss income inequality, alternative ways to evaluate its size and trend over time, and how it might be affected by tax policy.  My job is to introduce you to the problem of defining income and to show how the definition affects our understanding of inequality.

To eliminate suspense from the start: Nothing I am about to say undermines the popular narrative about recent inequality trends.  For the past 35 years, U.S. inequality has increased.  Inequality has increased noticeably, no matter what income definition you care to use.  A couple of things you read in the newspaper are untrue under some income definitions. For example, under a comprehensive income definition it is false to claim that all the income gains of the past 2 or 3 decades have gone to the top 1 percent, or the top 5 percent, or the top 10 percent of income recipients.  Middle- and low-income Americans have managed to achieve income gains, too, as we shall see.

Tax policy certainly affects overall inequality, but I shall leave it for Scott, David, and Tracy to take that up. Let me turn to my main job, which is to distinguish between different reasonable income measures.

The crucial thing to know is that contradictory statements can be made about some income trends because of differences in the definition of income.  In general, the most pessimistic statements about trends rely on an income definition that is restrictive in some way.  The definition may exclude important income items, items, for example, that tend to equalize or boost family incomes.  The definition may leave out adjustments to income … adjustments that tend to boost the rate of income gain for low- or middle-income recipients, but not for top-income recipients.

The narrowest income definition commonly used to evaluate income trends is Definition #1 in my slide, “pretax private, cash income.”  Columnists and news reporters are unknowingly using this income definition when they make pronouncements about the income share of the “top 1 percent.”  The data about income under this definition are almost always based on IRS income tax returns, supplemented with a bit of information from the Commerce Department’s National Income and Product Account (NIPA) data file.

The single most common income definition used to assess income trends and inequality is the Census Bureau’s “money income” definition, Definition #2 on the slide.  It is just the same as the first definition I mentioned, except this income concept also includes government cash transfer payments – Social Security, unemployment insurance, cash public assistance, Veterans’ benefits, etc.

A slightly more expansive definition (#3) also adds food stamp (or SNAP) benefits plus other government benefits that are straightforward to evaluate. Items of this kind include the implicit rent subsidy low-income families receive in publicly-subsidized housing, school lunch subsides, and means-tested home heating subsidies.

Now we come to subtractions from income. These typically reflect families’ tax obligations.  The Census Bureau makes estimates of state and federal income tax liabilities as well as payroll taxes owed by workers (though not by their employers).  Since income and payroll taxes subtract from the income available to pay for other stuff families want to buy, it seems logical to also subtract them from countable income. This is done under income Definition #4.  Some tax obligations – notably the Earned Income Credit (EIC) – are in fact subtractions from taxes owed, which would not be a problem in the case of families that still owe positive taxes to the government.  However, the EIC is refundable to taxpayers, meaning that some families have negative tax liabilities:  The government owes them money.  In this case, if you do not take taxes into account you understate low-income families’ incomes, even as you’re overstating the net incomes available to middle- and high-income families.

Now let’s get a bit more complicated.  Forget what I said about taxes, because our next income definition (#5) also ignores them.  It is an even-more-comprehensive definition of gross or pretax income.  In addition to all those cash and near-cash items I mentioned in Definition #3, Definition #5 includes imputed income items, such as: 

• The value of your employer’s premium contribution to your employee health plan;
• The value of the government’s subsidy to your public health plan – Medicare, Medicaid, state CHIP plans, etc.
• Realized taxable gains from the sale of assets; and
• Corporate income that is earned by companies in which you own a share even though it is not income that is paid directly to you.

This is the most comprehensive income definition of which I am aware that refers to gross or pre-tax income.

Finally we have Definition #6, which subtracts your direct and indirect tax payments.  The only agency that uses this income definition is principally interested in the Federal budget, so the subtractions are limited to Federal income and payroll taxes, Federal corporate income taxes, and excise taxes.

Before we go into why you should care about any of these definitions, let me mention a somewhat less important issue, namely, how we define the income-sharing group over which we estimate inequality.  The most common assessment unit for income included under Definition #1 (“Pre-tax private cash income”) is the Federal income tax filing unit.  Sometimes this unit has one person; sometimes 2 (a married couple); and sometimes more than 2, including dependents.

The Census Bureau (and, consequently, most users of Census-published statistics) mainly uses “households” as reference units, without any adjustment for variations in the size of different households.  The Bureau’s median income estimate, for example, is estimated using the annual “money income” of households, some of which contain 1 person, some contain 2, some contain 3, and so on.

Many economists and sociologists find this unsatisfactory because they think a $20,000 annual income goes a lot farther if it is supporting just one person rather than 12.  Therefore, a number of organizations—notably, the Luxembourg Income Study (LIS), the Organisation of Economic Cooperation and Development (OECD), and the Congressional Budget Office (CBO)—assume household income is equally shared within each household, but that household “needs” increase with the square root of the number of people in the household.  That is, a household containing 9 members is assumed to require 1½ times as much income to enjoy the same standard of living as a family containing 4 members.  After an adjustment is made to account for the impact of household size, these organizations then calculate inequality among persons rather than among households.

How are these alternative income definitions estimated?  Who uses them?  What do the estimates show?  I’ll only consider a two or three basic cases.

First, pretax, private, cash income. By far the most famous users of this definition are Professors Thomas Piketty and Emmanuel Saez.  Their most celebrated product is an annual estimate of the share of total U.S. income (under this restricted definition) that is received by the top 1 percent of tax filing units.

Here is their most famous chart, showing the income share of the top 1 percent going back to 1913. (I use the Piketty-Saez estimates that exclude realized capital gains in the calculation of taxpayers’ incomes.) The notable feature of the chart is the huge rise in the top income share between 1970—when it was 8 percent of all pretax private cash income—and last year—when the comparable share was 18 percent.  

I have circled one part of the line—between 1986 and 1988—to show you how sensitive their income definition is to changes in the income tax code.  In 1986 Congress passed the Tax Reform Act of 1986 (TRA86). By 1988 the reform was fully implemented.  Wealthy taxpayers noticed that TRA86 sharply reduced the payoff to holding corporate earnings inside a separately taxed corporate entity. Rich business owners or shareholders could increase their after-tax income by arranging things so their business income was taxed only once, at the individual level.  The result was that a lot of income, once earned by and held within corporations, was now passed through to the tax returns of rich individual taxpayers. These taxpayers appeared to enjoy a sudden surge in their taxable incomes between 1986 and 1988.  No one seriously believes rich people failed to get the benefits of this income before 1987.  Before 1987 the same income simply showed up on corporate rather than on individual income tax returns.

A final point:  The chart displayed in SLIDE #6 is the source of the widely believed claim that U.S. inequality is nowadays about the same as it was at the end of the Roaring 1920s, before the Great Depression.  That is close to being true – under this income definition.

Census “money income”: This income definition is very similar to the one just discussed, except that it includes cash government transfer payments.  The producer of the series is the Census Bureau, and its most famous uses are to measure trends in real median household income and the official U.S. poverty rate. Furthermore, the Census Bureau uses the income definition to compile estimates of the Gini coefficient of household income inequality and the income shares received by each one-fifth of households, ranked from lowest to highest income, and received by the top 5 percent of households.

Here is a famous graph based on the Bureau’s “median household income” series.  I have normalized the historical series using the 1999 real median income level (1999 and 2000 were the peak income years according to Census data).  Since 1999 and 2000, median income has fallen about 10 percent.  If we accept this estimate without qualification, it certainly represents bad news for living standards of the nation’s middle class. The conclusion is contradicted by other government income statistics that use a broader, more inclusive income definition, however.

And here is the Bureau’s most widely cited distributional statistic (after its “official poverty rate” estimate).  Since 1979, the Gini coefficient has increased 17 percent under this income definition. (It is worth noting, however, that the portion of the increase that occurred between 1992 and 1993 is mainly the result of methodological changes in the way the Census Bureau ascertained incomes in its 1994 income survey.)

When you hear U.S. inequality compared with that in other rich countries, the numbers are most likely based on calculations of the LIS or OECD.  Their income definition is basically “Cash and Near-cash Public and Private income minus Income and Payroll taxes owed by households.”  Under this income definition, the U.S. looks relatively very unequal and America appears to have an exceptionally high poverty rate.  U.S. inequality has been rising under this income definition, as indeed has also been the case in most other rich countries. The increase in the United States has been above average, however, helping us to retain our leadership position, both in income inequality and in relative poverty.

We turn last to the most expansive income definition:  CBO’s measure of net after-tax income.  I will use CBO’s tabulations using this income definition to shed light on some of the inequality and living standard trends implied by the narrower income definitions discussed above.

Let’s consider some potential limitations of a couple of those definitions.  The limitations do not necessarily make them flawed or uninteresting.  They do mean the narrower income measures cannot tell us some of the things that users claim they tell us.

An obvious shortcoming of the “cash pretax private income” definition is that it excludes virtually everything the government does to equalize Americans’ incomes.  Believe it or not, the Federal tax system is mildly progressive.  It claims a bigger percentage of the (declared) incomes of the rich than it does of middle-income families’ and especially the poor.  Any pretax income measure will miss that redistribution.

More seriously, it excludes all government transfer payments.  You may think the rich get a bigger percentage of their income from government handouts compared with middle class and poorer households.  That is simply wrong.  The rich get a lot less.  And the percentage of total personal income that Americans derive from government transfer payments has gone way up over the years.  In the Roaring 1920s, Americans received almost nothing in the form of government transfers. Less than 1 percent of Americans’ incomes were received as transfer payments.  By 1970—near the low point of inequality according to the Piketty-Saez measure—8.3 percent of Americans’ personal income was derived from government transfers.  Last year, the share was 17 percent. None of the increase in government transfers is reflected in Piketty and Saez’s estimates of the trend in inequality.  Inequality is nowadays lower than it was in the late 1920s, mainly because the government does more redistribution through taxes and transfers.

Both the Piketty-Saez and the Census “money income” statistics are affected by the exclusion of government- and employer-provided health benefits from the income definition. This slide contains numbers, starting in 1960, that show the share of total U.S. personal consumption consisting of personal health care consumption.  I have divided the total into two parts. The first is the share that is paid for out of our own cash incomes (the blue part at the bottom).  This includes our out-of-pocket spending for doctors’ charges, hospital fees, pharmaceutical purchases, and other provider charges as well as our out-of-pocket spending on health insurance premiums. The second is the share of our personal health consumption that is paid out of government subsidies to Medicare, Medicaid, CHIP, etc., or out of employer subsidies to employee health plans (the red part). 

As everyone knows, the share of total consumption that consists of health consumption has gone way up.  What few people recognize is that the share that is directly paid by consumers—through payments to doctors, hospitals, and household health insurance premium payments—has remained unchanged.  All of the increase in the health consumption share since 1960 has been financed through government and employer subsidies to health insurance plans. None of those government or employer contributions is counted as “income” under the Piketty-Saez and Census “money income” definitions.  You would have to be quite a cynic to claim the subsidies have brought households no living standard improvements since 1960, yet that is how they are counted under the Piketty-Saez and Census “money income” definitions.

Final slide: How much has inequality gone up under income definitions that count all income sources and subtract the Federal income, payroll, corporation, and excise taxes we pay?  CBO gives us the numbers, though unfortunately its numbers end in 2011.

Here are CBO’s estimates of real income gains between 1979 and 2011.  These numbers show that real net incomes increased in every income category, from the very bottom to the very top.  They also show that real incomes per person have increased much faster at the top—over on the right—than in the middle or at the bottom—over on the left.  Still, contrary to a common complaint that all the income gains in recent years have been received by folks at the top, the CBO numbers suggest net income gains have been nontrivial among the poor and middle class as well as among top income recipients.

Suppose we look at trends in the more recent past, say, between 2000 and 2011.  That lower panel in this slide presents a very different picture from the one implied by the Census Bureau’s “money income” statistics.  Unlike the “money income numbers” [SLIDE #9], these show that inequality has declined since 2000.  Unlike the “money income numbers” [SLIDE #8], these show that incomes of middle-income families have improved since 2000.  There are a variety of explanations for the marked contrast between the Census Bureau and CBO numbers.  But a big one is the differing income definitions the two conclusions are based on.  The more inclusive measure of income shows faster real income gains among middle-income and poorer households, and it suggests a somewhat different trend in inequality.

      

The pace of employment gains slowed in January from the torrid pace of the previous three months. The latest BLS jobs report shows that employers added 151,000 to their payrolls in January, well below monthly gains in October through December. In that quarter payrolls climbed almost 280,000 a month. For two reasons, the deceleration in employment gains was not a complete surprise. First, the rapid growth payrolls in the last quarter did not seem consistent with other indicators of growth in the quarter. Preliminary GDP estimates suggest that output growth slowed sharply in the fourth quarter compared with the previous two. Second, I see few indicators suggesting the pace of economic growth has picked up so far this year.

It’s worth noting that employment gains in January were far faster than needed to keep the unemployment rate from increasing. In fact, if payrolls continue to grow at January’s pace throughout the year, we should expect the unemployment rate to continue falling. As usual in the current expansion, private employers accounted for all of January’s employment gains. Government payrolls shrank slightly. The number of public employees is about the same as it was last July. Over the same period, private employers added about 213,000 workers a month to their payrolls. In January employment gains slowed in construction and in business and professional industries. Payrolls shrank in mining. Since mining payrolls reached a peak in September 2014, they have fallen 16 percent. Manufacturing payrolls rose slightly in January, but payroll gains have been very slow over the past year. Employment in the temporary help industry contracted in January. The industry has seen no net change in payrolls since October.

Average hourly pay in private companies edged up in January. The average nominal wage was 2.5 percent higher than its level 12 months earlier. This is a faster rate of improvement compared with what we saw earlier in the recovery, when annual pay gains averaged about 2.0 percent a year. The modest acceleration in nominal pay gains has occurred against the backdrop of slowing consumer price inflation. The combination has given workers real wage gains approaching 2.0 percent over the past year.

The BLS household survey showed a small drop in unemployment. The jobless rate fell to 4.9 percent, just 0.3 points above its average level in 2007, the last year before the Great Recession. The drop in unemployment was the result of a rise in the number of survey respondents who were employed. The labor force participation rate increased in January, and it has increased 0.3 points since October.

This rebound in labor force participation is modest compared with the drop that occurred between 2008 and 2015. From 2007 to January 2016 the adult participation rate fell 3.4 percentage points. Roughly half the drop is traceable to population aging, but the other half is due to factors related to the deep slump or to long-term factors that have affected Americans’ willingness to enter or remain in the workforce. If we assume all of the drop was due to factors that have temporarily discouraged jobless adults from seeking work, then we can recalculate the unemployment rate to reflect the rate we would see if all of these discouraged workers were reclassified as unemployed. That calculation suggests the current unemployment rate would be about 7.4 percent rather than 4.9 percent.

It is of course unlikely all the adults who’ve dropped out the labor force would stream back in if job finding got easier and real wages continued to rise. It is encouraging to see, however, that participation is now climbing after a long period of decline. Over the past four months, the labor force participation rate of 25-54 year-olds increased 0.5 percentage points.

      

Social Security is the most popular legacy of Franklin Roosevelt's New Deal. Last year almost 60 million Americans received benefits from the program. Payments amounted to over $875 billion, nearly a quarter of all federal spending.  For more than two decades, most discussion of Social Security, at least in Washington, has centered on its funding shortfall. Contributions to the program are not high enough to pay for all benefits scheduled under current law. The Social Security Trust Fund is expected to be depleted around 2030. If Congress does not address the funding problem before reserves are exhausted, monthly payments will have to be cut about one-fifth.

Despite the projected shortfall, Democrats in Congress have begun to argue that Social Security benefits should be expanded rather than cut.  Senators Bernie Sanders and Brian Schatz have offered proposals to boost monthly pensions while at the same time shoring up Social Security finances through tax hikes on high-income Americans. 

That Democratic voters and lawmakers embrace these ideas is not surprising. But opinion polling suggests such reforms also enjoy broad support among self-identified independents and Republicans. For example, 57 percent of Republicans (versus 71 percent of Democrats) favor increasing cost-of-living adjustments in the benefit formula. Forty-eight percent of Republicans (versus 67 percent of Democrats) favor boosting the minimum benefit available to low-wage workers who have contributed for many years to the program.  Seventy-four percent of Republicans (versus 88 percent of Democrats) favor raising taxes in order to protect benefits. These polling numbers were obtained in 2013, but more recent polls show similar opinions. Even if debates among Washington insiders and GOP lawmakers focus on how to trim benefits in order to keep Social Security solvent, poll results suggest Senator Sanders holds views closer to those of the typical voter.

One question for both voters and policymakers is whether the aged population is really the most deserving target for additional government spending.  Much of the discussion of voter disaffection in the current election cycle has focused on the stagnation of middle class incomes and the rise in inequality.  While these represent major problems for families headed by a working-age person, they have not been notably troublesome for the nation’s elderly.  The incomes of the elderly, unlike those of the nonelderly, have increased steadily over the past three or four decades.  For low- and middle-income retirees, incomes have clearly improved. The same cannot be said for the incomes of low- and middle-income working-age families. Income inequality among the elderly has increased, to be sure, but much more slowly than among working-age families.

In new research with my colleagues Barry Bosworth and Kan Zhang, I have examined trends in real incomes and inequality among the nation’s elderly and compared them with the same trends in working-age families. We show that inequality has increased among both the elderly and nonelderly, but it has increased much faster among families headed by prime-age and younger adults than among families headed by someone past age 62.  More to the point, real money incomes have increased much faster among middle- and low-income aged families compared with middle- and low-income working-age families. 

Our estimates of the annual rate of change in real money income are displayed in the chart below. The changes are estimated over the period from 1979 to 2012 based on data reported in the Census Bureau’s annual income survey. The top panel shows changes in families with a head who is less than 62. The bottom panel shows changes in families with a head older than 62.  Each bar shows the annual rate of change in real income at the indicated position of the income distribution, either for nonaged families (in the top panel) or for aged families (in the bottom panel).  At the top of the two income distributions—that is, at the 98^th income percentile—real income gains are virtually the same in the two groups.  Further down the income ladder, the income gains differ noticeably, with bigger differences the further down we go.  Middle- and low-income working-age families have clearly fared much worse than families with an equivalent position in the old-age income distribution.

Estimates of income growth based solely on pre-tax cash incomes, such as the ones in the chart, almost certainly understate the improvement families have seen in their living standards, as I have argued elsewhere (here and here).  However, the understatement is bigger in the case of elderly and low-income Americans than it is for the nonelderly and affluent.  If we adjust family incomes to reflect the taxes families owe and the monetary value of their noncash benefits, the relative improvement in the standard of living of older Americans is even greater than is shown in the chart. Under almost any plausible income definition, the elderly have fared better than the nonelderly, especially at the bottom of the income distribution.

The income statistics do not prove the policy reforms urged by Congressional Democrats are unneeded or undesirable. Their proposals spring from an accurate reading of a long-term trend toward less pension coverage — ironically, a trend that has mainly affected working-age adults.  Whereas workers in the 1950s through the 1970s enjoyed continuous improvement in their access to employer-provided retirement benefits, the improvement ceased after 1980. Since that time, private-sector workers have seen reductions in the coverage and generosity of their employer-sponsored pensions. If the private sector voluntarily provides less retirement protection, it does not seem unreasonable to expect the government to provide more.

A crucial reason the nation’s elderly population fared better compared with the nonelderly after 1980 is that Social Security and Medicare provided them government protection that was far more generous (and more costly to taxpayers) than the protection available to working-age adults and their youngsters. The gap was especially glaring in the case of families headed by low-wage breadwinners, who have suffered sizeable reductions in pay and employment opportunities. In the years since 1980, their losses have been only modestly compensated through changes in the tax code and expansions of public health insurance.

Changes in the labor market make it important to protect future retirement benefits provided through Social Security. The same labor market developments make it even more urgent to expand the employment opportunities and improve the protections and work supports offered to working-age breadwinners.  In 2016, the weakening of future income protection for the aged is mostly theoretical. In contrast, the sinking fortunes of less skilled working-age adults are anything but theoretical. They are plain to anyone who can read Census and Bureau of Labor Statistics reports. If taxpayers can identify additional resources to pay for major new initiatives, my vote is for programs that improve the prospects of struggling wage earners. The equity arguments for such an initiative seem to me more persuasive than the case for an across-the-board benefit hike targeted on retirees.

---

Editor's note: This piece originally appeared in Real Clear Markets. 

      

Researchers have long known that the rich live longer than the poor. Evidence now suggests that the life expectancy gap is increasing, at least here the United States, which raises troubling questions about the fairness of current efforts to protect Social Security.

There's nothing particularly mysterious about the life expectancy gap. People in ill health, who are at risk of dying relatively young, face limits on the kind and amount of work they can do. By contrast, the rich can afford to live in better and safer neighborhoods, can eat more nutritious diets and can obtain access to first-rate healthcare. People who have higher incomes, moreover, tend to have more schooling, which means they may also have better information about the benefits of exercise and good diet.

Although none of the above should come as a surprise, it's still disturbing that, just as income inequality is growing, so is life-span inequality. Over the last three decades, Americans with a high perch in the income distribution have enjoyed outsized gains.

Using two large-scale surveys, my Brookings colleagues and I calculated the average mid-career earnings of each interviewed family; then we estimated the statistical relationship between respondents' age at death and their incomes when they were in their 40s. We found a startling spreading out of mortality differences between older people at the top and bottom of the income distribution.

For example, we estimated that a woman who turned 50 in 1970 and whose mid-career income placed her in the bottom one-tenth of earners had a life expectancy of about 80.4. A woman born in the same year but with income in the top tenth of earners had a life expectancy of 84.1. The gap in life expectancy was about 3½ years. For women who reached age 50 two decades later, in 1990, we found no improvement at all in the life expectancy of low earners. Among women in the top tenth of earners, however, life expectancy rose 6.4 years, from 84.1 to 90.5. In those two decades, the gap in life expectancy between women in the bottom tenth and the top tenth of earners increased from a little over 3½ years to more than 10 years.

Our findings for men were similar. The gap in life expectancy between men in the bottom tenth and top tenth of the income distribution increased from 5 years to 12 years over the same two decades.

Rising longevity inequality has important implications for reforming Social Security. Currently, the program takes in too little money to pay for all benefits promised after 2030. A common proposal to eliminate the funding shortfall is to increase the full retirement age, currently 66. Increasing the age for full benefits by one year has the effect of lowering workers' monthly checks by 6% to 7.5%, depending on the age when a worker first claims a pension.

For affluent workers, any benefit cut will be partially offset by gains in life expectancy. Additional years of life after age 65 increase the number years these workers collect pensions. Workers at the bottom of the wage distribution, however, are not living much longer, so the percentage cut in their lifetime pensions will be about the same as the percentage reduction in their monthly benefit check.

Our results and other researchers' findings suggest that low-income workers have not shared in the improvements in life expectancy that have contributed to Social Security's funding problem.

It therefore seems unfair to preserve Social Security by cutting future benefits across the board. Any reform in the program to keep it affordable should make special provision to protect the benefits of low-wage workers.

Editor's note: This piece originally appeared in The Los Angeles Times. 

       

The latest BLS jobs report shows little sign employers are worried about the future strength of the recovery. Both the employer and household surveys suggest U.S. employers have an undiminished appetite for new hires. Nonfarm payrolls surged 242,000 in February, and upward revisions BLS employment estimates for January added almost 21,000 to estimated payroll gains in that month.

The household survey shows even bigger job gains in recent months. An additional 530,000 respondents said they were employed in February compared with January. This follows reported employment gains of 485,000 and 615,000 in December and January. Over the past year the household survey showed employment gains that averaged 237,000 per month. In comparison, the employer survey reported payroll gains averaging 223,000 a month.

These monthly gains are about three times faster than the job growth needed to keep the unemployment rate from climbing. As a result, the unemployment rate has fallen over the past year, reaching 4.9 percent in January. The jobless rate remained unchanged in February because of a continued influx of adults into the workforce. An additional 555,000 people entered the labor force, capping a three-month period which saw the labor force grow by over 500,000 a month. The labor force participation rate continued to inch up, rising 0.2 percentage points in February compared with the previous month. Since reaching a 38-year low in September 2015, the labor force participation rate has risen 0.5 points.

More than half the decline in the participation rate between the onset of the Great Recession and today is traceable to the aging of the adult population. A growing share of Americans are in late middle age or past 65, ages when we anticipate participation rates will decline. If we focus on the population between 25 and 54, the participation rate stopped declining in 2013 and has edged up 0.6 percentage points since hitting its low point. The employment-to-population rate of 25-54 year-olds has increased 3.0 percentage points since reaching a low in 2009 and 2010. Using the employment rate of 25-54 year-olds as an indicator of labor market tightness, we have recovered about 60 percent of the employment-rate drop that occurred in the Great Recession. Eliminating the rest of the decline will require a further increase in prime-age labor force participation.

Two other indicators suggest the job market remains some distance from a full recovery. More than a quarter of the 7.8 million unemployed have been jobless 6 months or longer. The number of long-term unemployed is about 70 percent higher than was the case just before the Great Recession. Nearly 6 million Americans who hold part-time jobs indicate they want to work on full-time schedules. They cannot do so because they have been assigned part-time hours or can only find a part-time job. The number of workers in this position is more than one-third higher than the comparable number back in 2007. Nonetheless, nearly all indicators of labor market tightness have displayed continued improvement in recent months.

February’s surge in employment growth and labor force participation was accompanied by an unexpected drop in nominal wages. Average hourly pay fell from $25.38 to $25.35 per hour. Compared with average earnings 12 months ago, workers saw a 2.2 percent rise in nominal hourly earnings. Because inflation is low, this probably translates into a real wage gain of about 1 percent. While employers may have an undiminished appetite for new hires, they show little inclination to boost the pace of wage increases.

       

Ahead of Tuesday’s primary elections in Ohio, Florida and other states, the 2016 presidential candidates have been talking about the future of Social Security and its funding shortfalls.

Over the next two decades, the money flowing into Social Security will be too little to pay for all promised benefits. The reserve fund will be exhausted soon after 2030, and the only money available to pay for benefits will be from taxes earmarked for the program. Unless Congress and the President change the law before the reserve is depleted, monthly benefits will have to be cut about 21%.

Needless to say, office holders, who must face voters, are unlikely to allow such a cut. Before the Trust Fund is depleted, lawmakers will agree to some combination of revenue increase and future benefit reduction, eliminating the need for a sudden 21% pension cut. The question is: what combination of revenue increases and benefit cuts does each candidate favor?

The candidate offering the most straightforward but least credible answer is Donald Trump. During the GOP presidential debate last week, he pledged to do everything within his power to leave Social Security “the way it is.” He says he can do this by making the nation rich again, by eliminating budget deficits, and by ridding government programs of waste, fraud, and abuse. In other words, he proposed to do nothing specifically to improve Social Security’s finances. Should Trump’s deal-making fail to make us rich again, he offered no back-up plan for funding benefits after 2034.

The other three GOP candidates proposed to repair Social Security by cutting future pensions. No one in the debate, except U.S. Sen. Marco Rubio from Florida, mentioned a specific way to accomplish this. Rubio’s plan is to raise the age for full retirement benefits. For many years, the full retirement age was 65. In a reform passed in 1983, the retirement age was gradually raised to 66 for people nearing retirement today and to 67 for people born after 1960. Rubio proposes to raise the retirement age to 68 for people who are now in their mid-40s and to 70 for workers who are his children’s age (all currently under 18 years old).

In his campaign literature, Rubio also proposes slowing the future rate of increase in monthly pensions for high-income seniors. However, by increasing the full retirement age, Rubio’s plan will cut monthly pensions for any worker who claims benefits at 62 years old. This is the earliest age at which workers can claim a reduced pension. Also, it is by far the most common age at which low-income seniors claim benefits. Recent research suggests that low-income workers have not shared the gains in life expectancy enjoyed by middle- and especially high-income workers, so Rubio’s proposed cut could seriously harm many low-income workers.

Though he didn’t advertise it in the debate, Sen. Ted Cruz favors raising the normal retirement age and trimming the annual cost-of-living adjustment in Social Security. In the long run, the latter reform will disproportionately cut the monthly pensions of the longest-living seniors. Many people, including me, think this is a questionable plan, because the oldest retirees are also the most likely to have used up their non-Social-Security savings. Finally, Cruz favors allowing workers to fund personal-account pensions with part of their Social Security contributions. Although the details of his plan are murky, if it is designed like earlier GOP privatization plans, it will have the effect of depriving Social Security of needed future revenues, making the funding gap even bigger than it is today.

The most revolutionary part of Cruz’s plan is his proposal to eliminate the payroll tax. For many decades, this has been the main source of Social Security revenue. Presumably, Cruz plans to fund pensions out of revenue from his proposed 10% flat tax and 16% value-added tax (VAT). This would represent a revolutionary change because up to now, Social Security has been largely financed out of its own dedicated revenue stream. By eliminating the independent funding stream, Cruz will sever the perceived link between workers’ contributions and the benefits they ultimately receive. Most observers agree with Franklin Roosevelt that the strong link between contributions and benefits is a vital source of the enduring popularity of the program. Social Security is an earned benefit for retirees rather than a welfare check.

Gov. John Kasich does not propose to boost the retirement age, but he does suggest slowing the growth in future pensions by linking workers’ initial pensions to price changes instead of wage changes. He hints he will impose a means test in calculating pensions, reducing the monthly pensions payable to retirees who have high current incomes. Many students of Social Security think this a bad idea, because it can discourage workers from saving for retirement.

All of the Republican candidates, except Trump, think Social Security’s salvation lies in lower benefit payouts. Nobody mentions higher contributions as part of the solution. In contrast, both Democratic candidates propose raising payroll or other taxes on workers who have incomes above the maximum earnings now subject to Social Security contributions. This reform enjoys broad support among voters, most of whom do not expect to pay higher taxes if the income limit on contributions is lifted. Sen. Bernie Sanders would immediately spend some of the extra revenue on benefit increases for current beneficiaries, but his proposed tax hike on high-income contributors would raise enough money to postpone the year of Trust Fund depletion by about 40 years. Hillary Clinton is less specific about the tax increases and benefit improvements she favors. Like Sanders, however, she would vigorously oppose benefit cuts.

None of the candidates has given us a detailed plan to eliminate Social Security’s funding imbalance. At this stage, it’s not obvious such a plan would be helpful, since the legislative debate to overhaul Social Security won’t begin anytime soon. Sanders has provided the most details about his policy intentions, but his actual plan is unlikely to receive much Congressional support without a massive political realignment. Cruz’s proposal, which calls for eliminating the Social Security payroll tax, also seems far outside the range of the politically feasible.

What we have learned from the GOP presidential debates so far is that Republican candidates, with the exception of Trump, favor balancing Social Security through future benefit cuts, possibly targeted on higher income workers, while Democratic candidates want to protect current benefit promises and will do so with tax hikes on high-income workers. There is no overlap in the two parties’ proposals, and this accounts for Washington’s failure to close Social Security’s funding gap.

Editor’s note: This piece originally appeared in Fortune.

       

The past few months have seen a flurry of reports on discouraging trends in life expectancy among some of the nation’s struggling populations. Different researchers have emphasized different groups and have tracked longevity trends over different time spans, but all have documented conspicuous differences between trends among more advantaged Americans compared with those in worse circumstances.

In a study published in April, Stanford economist Raj Chetty and his coauthors documented a striking rise in mortality rate differences between rich and poor. From 2001 to 2014, Americans who had incomes in the top 5 percent of the income distribution saw their life expectancy climb about 3 years. During the same 14-year span, people in the bottom 5 percent of the income distribution saw virtually no improvement at all.

Using different sources of information about family income and mortality, my colleagues and I found similar trends in mortality when Americans were ranked by their Social-Security-covered earnings in the middle of their careers. Over the three decades covered by our data, we found sizeable differences between the life expectancy gains enjoyed by high- and low-income Americans. For 50-year old women in the top one-tenth of the income distribution, we found that women born in 1940 could expect to live almost 6.5 years longer than women in the same position in the income distribution who were born in 1920. For 50-year old women in the bottom one-tenth of the income distribution, we found no improvement at all in life expectancy. Longevity trends among low-income men were more encouraging: Men at the bottom saw a small improvement in their life expectancy. Still, the life-expectancy gap between low-income and high-income men increased just as fast as it did between low- and high-income women.

One reason these studies should interest voters and policymakers is that they shed light on the fairness of programs that protect Americans’ living standards in old age. The new studies as well as some earlier ones show that mortality trends have tilted the returns that rich and poor contributors to Social Security can expect to obtain from their payroll tax contributions.

If life expectancy were the same for rich and poor contributors, the lifetime benefits workers could expect to receive from their contributions would depend solely on the formula that determines a worker’s monthly pensions. Social Security’s monthly benefit formula has always been heavily tilted in favor of low-wage contributors. They receive monthly checks that are a high percentage of the monthly wages they earn during their careers. In contrast, workers who earn well above-average wages collect monthly pensions that are a much lower percentage of their average career earnings.

The latest research findings suggest that growing mortality differences between rich and poor are partly or fully offsetting the redistributive tilt in Social Security’s benefit formula. Even though poorer workers still receive monthly pension checks that are a high percentage of their average career earnings, they can expect to receive benefits for a shorter period after they claim pensions compared with workers who earn higher wages. Because the gap between the life spans of rich and poor workers is increasing, affluent workers now enjoy a bigger advantage in the number of months they collect Social Security retirement benefits. This fact alone is enough to justify headlines about the growing life expectancy gap between rich and poor

There is another reason to pay attention to the longevity trends. The past 35 years have provided ample evidence the income gap between America’s rich and poor has widened. To be sure, some of the most widely cited income series overstate the extent of widening and understate the improvement in income received by middle- and low-income families. Nonetheless, the most reliable statistics show that families at the top have enjoyed faster income gains than the gains enjoyed by families in the middle and at the bottom. Income disparities have gone up fastest among working-age people who depend on wages to pay their families’ bills. Retirees have been better protected against the income and wealth losses that have hurt the living standards of less educated workers. The recent finding that life expectancy among low-income Americans has failed to improve is a compelling reason to believe the trend toward wider inequality is having profound impacts on the distribution of well-being in addition to its direct effect on family income.

Over the past century, we have become accustomed to seeing successive generations live longer than the generations that preceded them. This is not true every year, of course, nor is it always clear why the improvements in life expectancy have occurred. Still, it is reasonable to think that long-run improvements in average life spans have been linked to improvements in our income. With more money, we can afford more costly medical care, healthier diets, and better public health. Even Americans at the bottom of the income ladder have participated in these gains, as public health measures and broader access to health insurance permit them to benefit from improvements in knowledge. For the past three decades, however, improvements in average life spans at the bottom of the income distribution have been negligible. This finding suggests it is not just income that has grown starkly more unequal.

Editor's note: This piece originally appeared in Real Clear Markets.

       

No. We should exempt workers earning the lowest wages.

Social Security faces a serious funding problem. The program takes in too little money to pay all that has been promised to future beneficiaries. Government forecasters predict Social Security’s reserve fund will be depleted between 2030 and 2034. There are two basic ways we can eliminate the funding gap: cut benefits or increase contributions. A common proposal is to increase the age at which workers can claim full retirement benefits. For people nearing retirement today, the full retirement age is 66. As a result of a 1983 law, that age will rise to 67 for workers born after 1959.

When policymakers urge us to raise the retirement age, they are proposing to increase the full retirement age beyond 67, possibly to 70, for workers now in their 30s or 40s. This saves money, but it also cuts monthly retirement benefits by the same percentage for every worker, unless workers delay claiming benefits. The policy might seem fair if workers in future generations could all expect to share in gains in life expectancy. However, new research shows that gains in life expectancy have been very unequal, with the biggest improvements among workers who earn top incomes. Life expectancy gains for workers with the lowest incomes have been small or negligible.

If the full retirement age were raised, future retirees with high lifetime earnings can expect to receive some compensation when their monthly benefits are cut. Because they can expect to live longer than today’s retirees, they will receive benefits for a longer span of years after 65. For low-wage workers, there is no compensation. Since they are not living longer, their lifetime benefits will fall by the same proportion as their monthly benefits. Thus, “raising the retirement age” is a policy that cuts the lifetime benefits of future low-wage workers by a bigger percentage than it does of future high-wage workers.

The fact that low-wage workers have seen small or negligible gains in life expectancy signals that their health when they are past 60 is no better than that of low-wage workers born 20 or 30 years ago. This suggests their capacity to work past 60 is no better than it was for past generations. A sensible policy for cutting future benefits should therefore preserve current benefit levels for workers who have contributed to Social Security for many years but have earned low wages.

Editor's note: This piece originally appeared in CQ Researcher.

       

Unlike prime-age Americans, who have experienced declines in employment and labor force participation since the onset of the Great Recession, Americans past 60 have seen their employment and labor force participation rates increase.

In order to understand the contrasting labor force developments among the old, on the one hand, and the prime-aged, on the other, this paper develops and analyzes a new data file containing information on monthly labor force changes of adults interviewed in the Current Population Survey (CPS).

The paper documents notable differences among age groups with respect to the changes in labor force transition rates that have occurred over the past two decades. What is crucial for understanding the surprising strength of old-age labor force participation and employment are changes in labor force transition probabilities within and across age groups. The paper identifies several shifts that help account for the increase in old-age employment and labor force participation:

• Like workers in all age groups, workers in older groups saw a surge in monthly transitions from employment to unemployment in the Great Recession.
• Unlike workers in prime-age and younger groups, however, older workers also saw a sizeable decline in exits to nonparticipation during and after the recession. While the surge in exits from employment to unemployment tended to reduce the employment rates of all age groups, the drop in employment exits to nonparticipation among the aged tended to hold up labor force participation rates and employment rates among the elderly compared with the nonelderly. Among the elderly, but not the nonelderly, the exit rate from employment into nonparticipation fell more than the exit rate from employment into unemployment increased.
• The Great Recession and slow recovery from that recession made it harder for the unemployed to transition into employment. Exit rates from unemployment into employment fell sharply in all age groups, old and young.
• In contrast to unemployed workers in younger age groups, the unemployed in the oldest age groups also saw a drop in their exits to nonparticipation. Compared with the nonaged, this tended to help maintain the labor force participation rates of the old.
• Flows from out-of-the-labor-force status into employment have declined for most age groups, but they have declined the least or have actually increased modestly among older nonparticipants.

Some of the favorable trends seen in older age groups are likely to be explained, in part, by the substantial improvement in older Americans’ educational attainment. Better educated older people tend to have lower monthly flows from employment into unemployment and nonparticipation, and they have higher monthly flows from nonparticipant status into employment compared with less educated workers.

The policy implications of the paper are:

• A serious recession inflicts severe and immediate harm on workers and potential workers in all age groups, in the form of layoffs and depressed prospects for finding work.
• Unlike younger age groups, however, workers in older groups have high rates of voluntary exit from employment and the workforce, even when labor markets are strong. Consequently, reduced rates of voluntary exit from employment and the labor force can have an outsize impact on their employment and participation rates.
• The aged, as a whole, can therefore experience rising employment and participation rates even as a minority of aged workers suffer severe harm as a result of permanent job loss at an unexpectedly early age and exceptional difficulty finding a new job.
• Between 2001 and 2015, the old-age employment and participation rates rose, apparently signaling that older workers did not suffer severe harm in the Great Recession.
• Analysis of the gross flow data suggests, however, that the apparent improvements were the combined result of continued declines in age-specific voluntary exit rates, mostly from the ranks of the employed, and worsening reemployment rates among the unemployed. The older workers who suffered involuntary layoffs were more numerous than before the Great Recession, and they found it much harder to get reemployed than laid off workers in years before 2008. The turnover data show that it has proved much harder for these workers to recover from the loss of their late-career job loss.

Download "Labor Force Dynamics in the Great Recession and its Aftermath: Implications for Older Workers" »

Downloads

• Download "Labor Force Dynamics in the Great Recession and its Aftermath: Implications for Older Workers"

       

Alert voters everywhere realize the economy is neither as strong as claimed by the party in power nor the disaster described by the opposition. The election season will bring many passionate but dubious claims about economic trends. People running for office know that voters rank the economy near the top of their concerns. Of course, perceptions of the economy differ from one voter to the next. A few of us are soaring, more are treading water, and too many are struggling just to stay afloat.

Since reaching a low point in 2009, total U.S. output—as measured by real GDP—has climbed 15 percent, or about 2.1 percent a year. The recovery has been long-lived and steady, a tribute to the stewardship of the Administration and Federal Reserve. The economic rebound has also been disappointingly slow in view of the depth of the recession. GOP office seekers will mention this fact a number of times before November.

Compared with the worst months of the Great Recession, the unemployment rate has dropped by half. It now stands at a respectable 4.9 percent, almost 3 points lower than the rate when President Obama took office and far below the rate in fall 2009 when it reached 10 percent. Payroll employment has increased for 77 consecutive months. Since hitting a low in January 2010, the number of workers on employer payrolls has surged 14.6 million, or about 190,000 a month. While the job gains are encouraging, they have not been fast enough to bring the employment-to-population ratio back to its pre-recession level. June’s job numbers showed that slightly less than 80 percent of adults between 25 and 54 were employed. That’s almost 2 percentage points below the employment-to-population rate on the eve of the Great Recession.

One of the most disappointing numbers from the recovery has been the growth rate of wages. In the first 5 years of the recovery, hourly wages edged up just 2 percent a year. After factoring in the effect of consumer price inflation, this translates into a gain of exactly 0 percent. The pace of wage gain has recently improved. Workers saw their real hourly pay climb 1.7 percent a year in the two years ending in June.

The economic bottom line for most of us is the rate of improvement in our family income after accounting for changes in consumer prices. No matter how household income is measured, income gains have been slower since 2007 than they were in earlier decades. The main reason is that incomes produced in the market—in the form of wages, self-employment income, interest, dividends, rental income, and realized capital gains—fell sharply in the Great Recession and have recovered very slowly since then. That a steep recession would cause a big drop in income is hardly a surprise. Employment, company profits, interest rates, and rents plunged in 2008 and 2009, pushing down the incomes Americans earn in the market. The bigger surprise has been the slow recovery of market income once the recession was behind us.

Some critics of the recovery argue that the income gains in the recovery have been highly skewed, with a disproportionate share obtained by Americans at the top of the income ladder. Economist Emmanuel Saez tabulates U.S. income tax statistics to track market income gains at the top of the distribution. His latest estimates show that between 2009 and 2015 income recipients in the top 1 percent enjoyed real income gains of 24 percent. Among Americans in the bottom nine-tenths of the income distribution, average market incomes climbed only 4 percent.

URL = http://eml.berkeley.edu/~saez/TabFig2015prel.xls.

However, Saez’s estimates also show that top income recipients experienced much bigger income losses in the Great Recession. Between 2007 and 2009 they saw their inflation-adjusted incomes drop 36 percent (see Chart 1). In comparison, the average market income of Americans in the bottom nine-tenths of the distribution fell just 12 percent. These numbers mean that top income recipients have not yet recovered the income losses they suffered in the Great Recession. In 2015 their average market income was still 13 percent below its pre-recession level. For families in the bottom nine-tenths of the distribution, market income was “only” 8 percent below its level in 2007.

Only about half of households rely solely on market income to support themselves. The other half receives income from government transfers. What is more, this fraction tends to increase in bad times. Many retirees rely mainly on Social Security to pay their bills; they depend on Medicare or Medicaid to pay for health care. Low-income Americans often have little income from the market, and they may rely heavily on public assistance, food stamps, or government-provided health insurance. When joblessness soars the percentage of families receiving government benefits rises, largely because of increases in the number of workers who collect unemployment insurance.

Government benefits, which are not counted in Saez’s calculations, replace part of the market income losses families experience in a weak economy. As a result, the net income losses of most families are much smaller than their market income losses. The Congressional Budget Office (CBO) recently published statistics on market income and before-tax and after-tax income that shed light on the size and distribution of household income losses in the Great Recession and ensuing recovery. The tabulations show that, except for households at the top of the distribution, net income losses were far smaller than the losses indicated in Saez’s income tax data.

URL = https://www.cbo.gov/sites/default/files/114th-congress-2015-2016/reports/51361-SupplementalData-2.xlsx.

For example, among households in the middle fifth of the before-tax income distribution, average market income fell more than 10 percent in the Great Recession (see Chart 2). If we include government transfers in the income definition, average income fell 4.4 percent. If we account for the federal taxes families pay, average net income fell just 1 percent. In contrast, among households in the top 1 percent of the distribution, average market income fell 36 percent, average income including government transfers fell 36 percent, and average income net of federal taxes fell 37 percent. Government transfers provided little if any protection to top-income households.

The CBO income statistics end in 2013, so they do not tell us how net income gains have been distributed in the last couple of years. Nonetheless, based on Saez’s income tax tabulations it is very unlikely top income recipients have recovered the net income losses they experienced in the Great Recession. All the available statistics show household income gains since 2007 have been negligible or small, and this is true across the income distribution.

It is popular to say slow income gains in the middle and at the bottom of the distribution are due to outsize income gains among families at the top. While this story is at least partly true for the three decades ending in 2007, it does not fit the facts for the years since 2007. CBO’s latest net income tabulations show that inequality was almost 5 percent lower in 2013 than it was in 2007. The Great Recession hurt the incomes of Americans up and down the income distribution, but the biggest proportional income losses were at the very top. To be sure, income gains in the recovery after 2009 have been concentrated among top income recipients. Even so, their income losses over the recession and recovery have been proportionately bigger than the losses suffered by middle- and low-income families.

---

Editor's note: This piece originally appeared in Real Clear Markets.

       

       

       

       

       

       

       

       

       

       

       

       

       

       

       

       

Washington is again paralyzed and pulling back on clean energy economic development. Deficit politics and partisanship are firmly entrenched and the raft of federal financial supports made available through the 2009 stimulus law and elsewhere is starting to expire.

No wonder it’s hard to imagine—especially if you’re sitting in the nation’s capital—how the next phase of American clean energy industry growth will be financed or its next generation of technologies and firms supported.

And yet, one source of action lies hidden in plain sight. With federal clean energy activities largely on hold, a new paper we are releasing today as part of the Brookings-Rockefeller Project on State and Metropolitan Innovation argues that U.S. states hold out tremendous promise for the continued design and implementation of smart clean energy finance solutions and economic development.

Specifically, we contend that the nearly two dozen clean energy funds (CEFs) now running in a variety of mostly northern states stand as one of the most important clean energy forces at work in the nation and offer at least one partial response to the failure of Washington to deliver a sensible clean energy development approach.

To date, over 20 states have created a varied array of these public investment vehicles to invest in clean energy pursuits with revenues often derived from small public-benefit surcharges on electric utility bills. Over the last decade, state CEFs have invested over $2.7 billion in state dollars to support renewable energy markets, counting very conservatively.  Meanwhile, they have leveraged another $9.7 billion in additional federal and private sector investment, with the resulting $12 billion flowing to the deployment of over 72,000 projects in the United States ranging from solar installations on homes and businesses to wind turbines in communities to large wind farms, hydrokinetic projects in rivers, and biomass generation plants on farms. 

In so doing, the funds stand well positioned—along with state economic development and other officials—to build on a pragmatic success and take up the challenge left by the current federal abdication of a role on clean energy economic development.

Yet here is the rub: For all the good the funds have achieved, project-only financing—as needed as it is—will not be sufficient to drive the growth of large and innovative new companies or to create the broader economic development taxpayers demand from public investments.  Also needed will be a greater focus on the deeper-going economic development work that can help spawn whole new industries. 

All of which points to the new brand of fund activity that our paper celebrates and calls for more of. 

In recent years, increasingly ambitious efforts in a number of states have featured engagement on at least three major fronts somewhat different from the initial fund focus: (1) cleantech innovation support through research, development, and demonstration (RD&D) funding; (2) financial support for early-stage cleantech companies and emerging technologies, including working capital for companies; and (3) industry development support through business incubator programs, regional cluster promotion, manufacturing and export promotion, supply chain analysis and enhancement, and workforce training programs.

These new economic development efforts—on display in California, Massachusetts, New York, and elsewhere—show the next era of state clean energy fund leadership coming into focus. States are now poised to jumpstart a new, creative period of expanded clean energy economic development and industry creation, to complement and build upon individualistic project financing. 

Such work could not be more timely at this moment of federal gridlock and market uncertainty.

Along these lines, then, our paper advances several recommendations for moving states more aggressively into this new period of clean energy economic development. We suggest that:

• States should reorient a significant portion (at least 10 percent of the total portfolio) of state CEF money to clean energy-related economic development

• States, as they reorient portions of their CEFS to economic development, should better understand the market dynamics in their metropolitan regions.  They need to lead by making available quality data on the number of jobs in their regions, the fastest-growing companies, the critical industry clusters, gaps in the supply chain for those industries, their export potential, and a whole range of economic development and market indicators

• States also should better link their clean energy funds with economic development entities, community development finance institutions (CDFIs), development finance organizations and other stakeholders who could be ideal partners to develop decentralized funding and effective economic development programs

In addition, we think that Washington needs to recognize the strength and utility of the CEFs and actively partner with them:

• The federal government should consider redirecting a portion of federal funds (for instance, from federal technology support programs administered by the Department of Energy and other programs meant for federal-state cooperation) to provide joint funding of cluster development, export programs, workforce training, and other economic development programs  through matching dollars to state funds that now have active economic development programs, and to provide incentives to states without such programs to create them

• The federal government should create joint technology partnerships with states to advance each state’s targeted clean energy technology industries, by matching federal deployment funding with state funding.

• The states and the federal government, more generally, should look to “decentralize” financing decisions to local entities with street knowledge of their industries, relying on more “development finance” authorities that have financed traditional infrastructure and now could finance new clean energy projects and programs

In sum, our new paper proposes a much greater focus in U.S. clean energy finance on “bottom up,” decentralized clean initiatives that rely on the states to catalyze regional economic development in regions. Such an approach—which reflects the emergence of an emerging “pragmatic caucus” in U.S. economic life—is currently demanded by federal inaction. However, it might also be the smartest, most durable way to develop the clean energy industries of the future without the partisan rancor and obtuseness that has stymied federal energy policy. State clean energy funds—having funded thousands of individual projects—bring significant knowledge to bear as they focus now on building whole industries. For that reason, the funds’ transition from project development to industry creation should be nurtured and supported.

       

State clean energy funds (CEFs) have emerged as effective tools that states can use to accelerate the development of energy efficiency and renewable energy projects. These clean energy funds, which exist in over 20 states, generate about $500 million per year in dedicated support from utility surcharges and other sources, making them significant public investors in thousands of clean energy projects.

However, state clean energy funds’ emphasis on a project finance model—which directly promotes clean energy project installation by providing production incentives and grants/rebates—is by itself not enough to build a statewide clean energy industry. State clean energy funds also need to pay attention to other critical aspects of building a robust clean energy industry, including cleantech innovation support through research and development funding, financial support for early-stage cleantech companies and emerging technologies, and various other industry development efforts.

As it happens, some of these state clean energy funds are already supporting a broader range of clean energy-related economic development activities within their states. As more and more states reorient their clean energy funds from a project finance-only model in order to encompass broader economic development activities, clean energy funds can collectively become an important national driver for economic growth.

To become true economic development engines in clean energy state clean energy funds should:

• Reorient a significant portion of their funding toward clean energy-related economic development
• Develop detailed state-specific clean energy market data
• Link clean energy funds with economic development entitites and other stakeholders in the emerging industry
• Collaborate with other state, regional, and federal efforts to best leverage public and private dollars and learn from each other's experiences

Downloads

• Download the Full Paper

       

With Washington adrift and the United Nations climate change panel again calling for action, the search for new clean energy finance solutions continues.  

Against this backdrop, the Metro Program has worked with state- and city-oriented partners to highlight such responses as repurposing portions of states’ clean energy funds and creating state green banks.  Likewise, the Center for American Progress just recently highlighted the potential of securitization and investment yield vehicles, called yield cos. And last week an impressive consortium of financiers, state agencies, and philanthropies announced the creation of the Warehouse for Energy Efficiency Loans (WHEEL) aimed at bringing low-cost capital to loan programs for residential energy efficiency. WHEEL is the country’s first true secondary market for home energy loans—and a very big deal. 

Another big deal is the potential of bond finance as a tool for clean energy investment at the state and local level. That’s the idea advanced in a new paper released this morning that we developed with practitioners at the Clean Energy Group and the Council for Development Finance Authorities.

Over 100 years, the nation’s state and local infrastructure finance agencies have issued trillions of dollars’ worth of public finance bonds to fund the construction of the nation’s roads, bridges, hospitals, and other infrastructure—and literally built America. Now, as clean energy subsidies from Washington dwindle, these agencies are increasingly willing to finance clean energy projects, if only the clean energy community will embrace them.

So far, these authorities are only experimenting. However, the bond finance community has accumulated significant experience in getting to scale and knows how to raise large sums for important purposes by selling bonds to Wall Street. Accordingly, the clean energy community—working at the state and regional level—should leverage that expertise. The challenge is for the clean energy and bond finance communities to work collaboratively to create new models for clean energy bond finance in states, and so to establish a new clean energy asset class that can easily be traded in capital markets.

Along these lines, our new brief argues that state and local bonding authorities, clean energy leaders, and other partners should do the following: 

• Establish mutually useful partnerships between development finance experts and clean energy officials at the state and local government levels
• Expand and scale up bond-financed clean energy projects using credit enhancement and other emerging tools to mitigate risk and through demonstration projects
• Improve availability of data and develop standardized documentation so that the risks  and rewards of clean energy investments can be better understood
• Create a pipeline of rated and private placement deals, in effect a new clean energy asset class, to meet the demand by institutional investors for fixed-income clean energy securities

And it’s happening. Already, bonding has been embraced in smart ways in New York; Hawaii; Morris County, NJ; and Toledo, among other locations featured in our paper. Now, it’s time for states and municipalities to increase the use of bonds for clean energy purposes. If they can do that it will be yet another instance of the nation’s states, metro areas, and private sector stepping up with a major breakthrough at a moment of federal inaction.

       

State and local bond finance represents a powerful but underutilized tool for future clean energy investment.

For 100 years, the nation’s state and local infrastructure finance agencies have issued trillions of dollars’ worth of public finance bonds to fund the construction of the nation’s roads, bridges, hospitals, and other infrastructure—and literally built America. Now, as clean energy subsidies from Washington dwindle, these agencies are increasingly willing to finance clean energy projects, if only the clean energy community will embrace them.

So far, these authorities are only experimenting. However, the bond finance community has accumulated significant experience in getting to scale and knows how to raise large amounts for important purposes by selling bonds to Wall Street. The challenge is therefore to create new models for clean energy bond finance in states and regions, and so to establish a new clean energy asset class that can easily be traded in capital markets. To that end, this brief argues that state and local bonding authorities and other partners should do the following:

• Establish mutually useful partnerships between development finance experts and clean energy officials at the state and local government levels
• Expand and scale up bond-financed clean energy projects using credit enhancement and other emerging tools to mitigate risk and through demonstration projects
• Improve the availability of data and develop standardized documentation so that the risks and rewards of clean energy investments can be better understood
• Create a pipeline of rated and private placement deals, in effect a new clean energy asset class, to meet the demand by institutional investors for fixed-income clean energy securities

Downloads

• Report
• Press Release