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The 2010 State New Economy Index

Massachusetts, Washington, Maryland, New Jersey and Connecticut are the top five states at the forefront of the nation’s movement toward a global, innovation-based new economy, according to The 2010 State New Economy Index, released in conjunction with The Information Technology and Innovation Foundation.

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The bottom five states were unchanged from 2008. Mississippi and West Virginia have lagged most in making the transition to the New Economy. The other lowest-scoring states include, in reverse order, Arkansas, Alabama, and Wyoming.

Methodology
Overall, the 2010 State New Economy Index uses 26 indicators, divided into five categories that best capture what is new about the New Economy:
1) Knowledge jobs
2) Globalization
3) Economic dynamism
4) Transformation to a digital economy
5) Technological innovation capacity

The State New Economy Index uses 26 indicators to assess states’ fundamental capacity to successfully navigate the shoals of economic change. It measures the extent to which state economies are knowledge-based, globalized, entrepreneurial, IT-driven, and innovation-based – in other words, to what degree state economies’ structures and operations match the ideal structure of the New Economy. The 2010 Index builds on four earlier Indexes, published in 1999, 2002, 2007, and 2008.

Regionally, the New Economy has taken the strongest hold in the Northeast, mid-Atlantic, Mountain West and Pacific regions; 13 of the top 20 states are in these four regions. In contrast, 18 of the 20 lowest-ranking states are in the Midwest, Great Plains and the South.

States at the top of the ranking not only have an abundance of high-tech firms, but also have a high concentration of managers, professionals, and college-educated residents working in “knowledge jobs” – those that require at least a two-year degree. And, even if their employment is not growing rapidly, all of the top-ranked states show above-average levels of entrepreneurship.

A large share of the leading states’ institutions and residents have embraced the digital economy. Most of these states also have a solid innovation infrastructure that fosters and supports technological innovation, and many have a good quality of life coupled with high levels of domestic and foreign immigration of highly skilled knowledge workers.

The lowest-ranking states tend to rely on natural resources or on mass-production manufacturing, and to depend on low costs rather than innovative capacity to gain advantage. But innovative capacity, derived through universities, R&D investments, scientists and engineers, and entrepreneurial drive, increasingly impels competitive success, the report said.

Lower-ranking states are not without opportunity, however. The IT revolution makes it easier for businesses to relocate, or start up and grow in less densely populated states farther away from existing agglomerations of industry and commerce. Because metropolitan areas in many of the top states suffer from high costs and near-gridlock on their roads, locating in less-congested metros, many in lower-ranking states, may be more attractive to entrepreneurial companies.

The report recommends that, to pursue this new approach to economic development, states should 1) establish policies that reduce within-state zero-sum competition; 2) implement state policies to spur “win-win” economic results; and 3) pursue new state-federal innovation-based economic development partnerships.

In addition to 2010, Massachusetts topped the four previous State Index lists. Washington, which ranked fourth in 2007 and in 2002, moves to second-place in 2010.  Maryland, with its high concentration of knowledge workers, maintains the third-place rank it held in 2007 and 2008. New Jersey’s strong pharmaceutical industry, coupled with a high-tech agglomeration around Princeton, an advanced services sector in Northern New Jersey, and high levels of inward foreign direct investment help drive it to fourth place (up from sixth in 2002 and fifth in 2008). Connecticut also moved up in the rankings, from sixth in 2008 to fifth in 2010.

Between 2007 and 2008, most states and the United States as a whole made sustained progress toward the New Economy. Of the 23 indicators that were comparable between 2008 and 2010, overall the United States increased on 14 and decreased on three, for a net increase of 11 indicators.

Personalized Health Manifesto and Personalized Health Project

Shifting to a health care paradigm that embraces healthy wellness and personalized health an be accelerated with a thoughtful and comprehensive plan to advance the science and practice of personalized health.

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David Ewing Duncan
Director, The Center for Life Science Policy, UC Berkeley

This document was prepared with the participation of thirty-five life science leaders representing science, medicine, business, government, patients, law, and the media (a complete list of participants appears at the end).

American society is on the cusp of a vital new era of health care, one in which medicine will shift from primarily addressing illness to a greater emphasis on prediction and prevention, and on individualized care. This historic transformation comes from a deepening understanding of biology, the emergence of new technologies, and a rising demand by individuals to understand and take charge of their own health. Yet a widening gap exists in integrating and implementing this promising new epoch of personalized health.

Resistance comes from traditions and attitudes that emerged during an age when medicine was limited primarily to diagnosing and treating disease, and by the prevailing use of drugs and protocols targeted more for populations and averages than for individuals. Even today, the biomedical enterprise overwhelmingly focuses on developing and paying for costly drugs, procedures, and devices that will be deployed after a person gets sick, with too little consideration for their personal physiology and circumstances.

This dominance is now being challenged. Discoveries in genomics, proteomics, environmental toxicology, microbiology, biocomputing, and many other fields are poised to provide unheard of insight into a person’s future health risks, and also to offer individualized options for improving health and wellness, and for managing disease.

Significant impediments and gaps remain however in applying this “new science”—not only in the clinic, but also in funding, infrastructure, regulation, law, business, education, and communication. Some of these gaps are unavoidable and naturally occur with any new discovery, while others are avoidable and potentially fixable.

A major hurdle is the unintended consequence of a system that has devoted considerable time and resources on basic research and on creating an ever more specialized phalanx of experts delving into the mechanisms of life. Over the years, this reductionist enterprise has produced critical insights that have made an age of personalized health possible. But it also has encouraged a parsing of knowledge and a silo effect that has made it difficult to capitalize on vast new stores of knowledge about human biology.

The time has come for an intensive focus on integration, the crucial complement to reductionism. Basic research and specialization remains crucial to the biomedical enterprise, but a reordering of priorities is required to stress the application and translation of what has been learned to improve health and to reduce health care costs.

Integration requires, first, a new urgency for scientists to work together to focus on the whole human organism; and, second, for society to absorb and implement scientific discoveries in the realms of clinical medicine, law, government, education, and commerce with greater creativity and resolve. To realize this vision will require coordination, funding, and a mandate for bold action.

To launch a new era of personalized health does not require a radical new blueprint for change. Rather, it can utilize an existing body of suggested proposals, reforms, and plans already put forth by individuals and organizations inside and outside of government. Some of these ideas have been tentatively initiated, but they require significantly more funding and support.

In order to accelerate a transformation to personalized health, we, the undersigned, call on the life science community, policymakers, patients, and society to take the following actions:

First, to acknowledge that:

  • New scientific discoveries are enabling a shift from a paradigm of health care based on illness to one equally centered on prediction, prevention, and personalized health.
  • A balance between specialization and integration needs to be restored, with an emphasis on the whole human organism as much as its parts, and as much on individual patients as populations.
  • Gaps exist that exacerbate the normal lag between discovery and application, both inside and outside the scientific community.
  • Shifting to a health care system based as much on healthy wellness as illness is achievable, and can be accelerated by systematic planning and proper funding.

Second, to advocate the following:

A Personalized Health Project that will:

  • Recruit key leaders from science, medicine, business, policy, government, patient advocacy, ethics, law, and the media;
  • Study and assess specific “gaps” between innovation and application, and assign task forces to address each substantial gap;
  • Create a blueprint for implementing specific initiatives and enhancing existing projects in the public and private sectors to support predictive and preventive health care; and,
  • Target, prioritize, and develop funding streams for the validation and application of new discoveries based on integrating individual discoveries and projects into a holistic model based on the needs of individuals and populations.

Third, to offer support for reforms in:

Education

  • Establish a new academic discipline focusing on the science of integration, including educational programs, funding, and journals.
  • Modify medical education and scientific training to emphasize wellness and predictive and preventive medicine, and a deeper understanding of the links between the new science and the clinic.
  • Provide incentives for medical trainees to pursue primary care and integrative fields such as medical genetics.
  • Organize an awareness campaign on the need to integrate fields within the life sciences and between the life sciences and society.

Policy

  • Refocus regulation and oversight to better utilize science and technology to streamline the drug and diagnostic approval process.
  • Embrace a new model based on predictive and preventive medicine, and personalized treatments.
  • Develop standard data elements for this new and emerging field.
  • Remove barriers to the flow of scientific information by adopting open source models for publishing studies and organizing databases.
  • Support improvements in information technology to better integrate data and to develop effective predictive models for populations and individuals.
  • Create methods and programs to assess the true cost-benefit of personalized health science and protocols.

Patient Participation

  • Encourage and enable the rise of the patient-consumer in health care.
  • Arm people with validated information on predictive and preventive tests and protocols, and on lifestyle options such as nutrition, diet, and exercise.

Business

  • Encourage entrepreneurs, investors, and commercial efforts to develop new products and protocols based on the science of personalized health.
  • Create a Human Integration Fund: a hybrid of public and private money dedicated to investing not in individual efforts, but in groupings of efforts that jointly target a disease or system, or the human body.

Reimbursement

  • Establish a reimbursement process that pays for and encourages predictive tests, prevention, healthy wellness, and targeted treatments.

Ethics and Global Health

  • Study the impact and the ethics of personalized health initiatives to ensure their adoption is safe and effective, and that privacy, personal choice, and access are protected.
  • Work to develop predictive and preventive strategies that are suitable for both the developed and developing world, and work to develop funding and initiatives for global personalized health.

End of Life

  • Acknowledge that illness and death remain a part of life, and continue a dedicated focus on personalized medicine to better customize treatment options, and encourage the use of palliative care where indicated.

Shifting to a health care paradigm that embraces healthy wellness and personalized health is a formidable challenge that will take many years. Yet we believe that this transformation can be accelerated with a thoughtful and comprehensive plan to advance the science and practice of personalized health, and that no time is better than now to launch this effort.

Expert Panel:

The following individuals participated in the development of the Personalized Health Manifesto and have endorsed it; neither they nor anyone else has had any editorial influence over this document.

ADAM GAZZALEY, MD, PhD, neurologist and neuroscientist, University of California at San Francisco

ANTHONY ATALA, MD, board member, Regenerative Medicine Foundation; director, Wake Forest Institute for Regenerative Medicine

ATUL BUTTE, MD, PhD, geneticist and bioinformaticist, Stanford University Medical School

BROOK BYERS, MBA, venture capitalist, Kleiner Perkins Caufield & Byers

CHRIS AUSTIN, MD, neurologist; director, Chemical Genomics Center, National Institutes of Health

DANIEL KRAFT, MD, PhD, oncologist; stem cell researcher, Stanford University Medical School

DAVID AGUS, MD, oncologist, proteomics researcher, entrepreneur, University of Southern California; co-founder, Navigenics

DAVID EWING DUNCAN, journalist and life science policy analyst; director, The Center for Life Science Policy, UC Berkeley

DIETRICH STEPHAN, PhD, geneticist; director, Ignite Institute; co-founder, Navigenics

EDWARD ABRAHAMS, PhD, president, Personalized Medicine Coalition

ERIC SCHADT, PhD, biocomputationist; chief scientific officer, Pacific Biosciences; co-founder, Sage Bionetworks

ERIC TOPOL, MD, cardiologist and translational geneticist; director, Scripps Translational Science Institute

FRANK DOUGLAS, MD, PhD, president and chief executive officer, Austen BioInnovation Institute of Akron, Ohio; founder and first executive director of the MIT Center for Biomedical Innovation, Massachusetts Institute of Technology; former chief scientific officer, Aventis

FRED FRANK, MBA, life sciences investment banker; vice chairman, Peter J. Solomon Company; former vice chairman, Lehman Brothers

GEORGE CHURCH, PhD, molecular biologist, professor of Genetics, and director, Center for Computational Genetics, Harvard Medical School

GEORGE POSTE, PhD, researcher, policy analyst, and former pharmaceutical executive; chief scientist, Complex Adaptive Systems Initiative and professor of Health Innovation, Arizona State University; former president, R&D, of SmithKline Beecham

GREG SIMON, JD, senior vice president for Worldwide Policy, Pfizer; former president, FasterCures; former chief domestic policy advisor to Vice President Al Gore

GREGORY STOCK, PhD, MBA, founding CEO, Signum Biosciences; founding director, Program on Medicine, Technology and Society, University of California at Los Angeles School of Medicine

HANK GREELY, JD, professor of Law, Stanford University; director, Center for Law and the Biosciences

JAMES HEYWOOD, co-founder and chairman, PatientsLikeMe

JAMES THOMSON, VMD, PhD, stem cell scientist; director of Regenerative Biology, The Morgridge Institute for Research, University of Wisconsin School of Medicine and Public Health

JOSHUA ADLER, MD, physician, chief medical officer, University of California at San Francisco Medical Center

LEE HOOD, MD, PhD, molecular biologist and bioinformaticist; founder and director, Institute for Systems Biology

MARGARET ANDERSON, executive director, FasterCures

MARTYN SMITH, PhD, professor of Toxicology, School of Public Health, Division of Environmental Health Sciences, University of California Berkeley

MICHAEL ROIZEN, MD, preventive medicine; director, Wellness Institute, Cleveland Clinic

MISHA ANGRIST, PhD, assistant professor, Duke University Institute for Genome Sciences & Policy

NATHANIEL DAVID, PhD, entrepreneur and venture capitalist; venture partner, Arch Venture Partners

PAUL BILLINGS, MD, PhD, clinical geneticist; director, Genomic Medicine Institute, El Camino Hospital

RAY WOOSLEY, MD, PhD, president and CEO, Critical Path Institute

SAFI BAHCALL, PhD, entrepreneur; CEO, Synta Pharmaceuticals Corp

STEPHEN FRIEND, MD, PhD, president, CEO, co-founder, Sage Bionetworks; former senior vice president and franchise head for Oncology Research, Merck

STEPHEN SPIELBERG, MD, PhD, pediatrician; director, Center for Personalized Medicine and Therapeupic Innovation, Children’s Mercy Hospital, Kansas City, Missouri; former dean, Dartmouth Medical School

STEVE WIGGINS, venture capitalist and former health insurance executive; managing director of Essex Woodlands Health Ventures; founder and former CEO, Oxford Health Plans

ZACK LYNCH, executive director, Neurological Industry Organization

Putting Performance on the Map

This report IFF found that 88 percent of students in District and charter public schools within the Kansas City, Missouri School District (KCMSD) boundaries did not attend a school that met Missouri state standards for academic performance.

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Executive Summary

This is a time of momentous change for schools across the country. Shrinking budgets and enrollments accompanied by poor performance create challenges for the education environment. The Kansas City region is not exempt. At the end of the 2009-2010 school year, the Kansas City, Missouri School District (KCMSD) closed, restructured, or moved 23 elementary and secondary schools. Three new charter schools opened in 2009-2010 for a total of 27 within the KCMSD boundaries. In addition, teacher and staff lay-offs are frequent occurrences and a number of KCMSD and charter schools are experiencing sanctions related to low-performance. In fact, there are few schools, District or charter, that perform at Missouri state standards. This leaves many children who live in the boundaries of the KCMSD without the option of a quality school. Putting Performance on the Map presents important data on enrollment, capacity, location, and performance in KCMSD and charter public schools, and addresses the effect of the District’s transformation plan on students who live within the KCMSD boundaries.

IFF’s fundamental belief and the premise of this report is that all children living within the KCMSD boundaries deserve to attend a school that performs at least at Missouri state standards in the neighborhood where they reside. However, 88 percent of students in the district attend schools, both KCMSD, and charter, where performance lags behind this state standard.

The methodology used in this report, developed by IFF in 2003, identifies which zip codes have the greatest need for performing schools. Capacity in better performing schools in each zip code is compared against school enrollment as well as against the total school-age population. This determines how well schools are able to serve students who reside in a given zip code, as well as the potential to serve all children in that zip code. These comparisons create service levels, the percent of students who can be served by these better schools, and service gaps, the number of students who cannot be served by these schools. The service levels and service gaps are then compared and ranked, and the five zip codes with the greatest need are mapped to highlight geographic concentrations of need in KCMSD.

The ranking of need in the district provides critical information to inform community, policymakers, educators, parents, and business leaders on the importance of prioritizing their efforts to ensure that all students living in the KCMSD boundaries have a seat in a performing school.

Key Findings

  • KCMSD schools performing at state standard (Level I) can serve 2,704 or 15.4 percent of KCMSD students. To serve all 17,517 KCMSD students residing in the district, the KCMSD needs an additional 14,813 performing seats.
  • KCMSD schools performing between 50 and 99 percent of the state standard (Levels II and III) provide 6,534 seats of capacity, which when combined with Level I seats, create a total of 9,238 seats that can serve 52.7 percent of KCMSD students. Even with this additional capacity, the District needs 8,279 performing seats to serve all of its students.
  • Without the capacity in KCMSD’s selective schools, the District has an even greater need for performing seats. Eighty-one percent of the need in schools that are open to all students, 8,340 of 10,283 seats, is in five zip codes, 64128, 64127, 64130, 64110, and 64124, where the majority of KCMSD students reside.
  • The majority of charter school students, 5,490, attend schools that perform below 50 percent of the state standard (Level IV). One charter school meets state standards (Level I) and enrolls 479 students, and 2,518 students attend Level II and III charter schools.
  • There are a combined 12,235 seats in Level I-III KCMSD and charter schools. This is enough to serve 47.1 percent of the 26,004 students enrolled in KCMSD and charter schools, and 34.6 percent of the 35,337 school-age children in the district.
  • There are no non-selective high schools that meet state standards, and there are only 564 seats in non-selective, Level II-III high schools, all of which are in charter schools.
  • There is excess capacity of 4,500 seats in KCMSD’s Level I-III schools. Despite the restructuring of schools planned for 2010-2011, there will remain an estimated 3,462 seats of excess capacity in these better-performing schools.
  • The KCMSD plan, approved in March 2010, will affect 5,300 of the 17,500 students enrolled in KCMSD schools. The plan creates a loss of 1,383 Level I-III seats by closing or restructuring three Level I-III schools, including the absorption of Level I Lincoln College Preparatory Middle School by Lincoln College Preparatory High School. However, the majority of schools to be closed perform below 50 percent of the state standard.

Observations and Action Steps

Observation: The KCMSD plan reduces the financial burden of underused school buildings and attempts to address the challenge of the lowest-performing schools. However, it also reduces the number of seats in better performing schools and leaves empty thousands of others.

Action Step: Fill empty seats in Level I-III schools and encourage all students who are eligible to attend KCMSD’s two restructured selective schools.

Observation: The majority of charter school students (5,490 or 64.7 percent) are in a Level IV school. In many communities, charter schools are a model that increases students’ access to better public schools, but many of Kansas City’s charters have existed for 10 years and are still not able to reach even half of state standards. The Department of Elementary and Secondary Education (DESE) and the charter school sponsors should develop a strategy to close charter schools with consistently poor achievement and consider replacing them with proven or promising new charter models.

Action Step: Accountability measures should be taken to close the lowest-achieving charter schools and replace them with proven or promising new ones. Non-renewal of low-performing charters should be considered by charter school sponsors and facilitated by DESE.

Observation: Charter schools in Kansas City are not coordinated strategically with regard to location and growth plans. Three-quarters of the seats in charter schools in the 2008-2009 school year are located outside the district’s high-need zip codes. Charter schools should be an integrated part of overall school reform efforts and/or choice plans, including those led by the KCMSD itself.

Action Step: A strategy for charter school growth should be developed by Kansas City education leaders. Charter schools should only be approved by DESE if they can demonstrate how they intend to fill a geographic need or a specific void in the communities they intend to serve.

Observation: Given the number of low-performing charter schools, the District may attract students back from charters if the transformation goals are met and communicated to parents. By analyzing the role and performance of charter schools, KCMSD can bring together charter school sponsors to ensure that wherever possible the goals of the District are aligned with charter schools.

The KCMSD sits at a crossroads. After years of shrinking enrollment and delaying difficult decisions, it faces a financial crisis that is now being confronted by new leadership. But as Putting Performance on the Map shows, the KCMSD faces an academic crisis of equal proportion that demands equal commitment and energy. Eighty-five percent of KCMSD students do not attend a school that meets Missouri state standards, and the need for seats in performing schools is concentrated in five zip codes where over half of KCMSD students reside but where there are no schools open to all students that are performing at state standards. At the same time, charter schools are not providing a better alternative to poor-performing District schools, as they do in many other communities. The majority of charter schools in the district are failing to reach even half the state performance standards.

Putting Performance on the Map provides crucial, community-level information that can help the KCMSD, charter school sponsors and operators, and all education stakeholders prioritize and chart a path forward—one that provides children in every KCMSD neighborhood with the performing schools they deserve.

After Inception: How Enduring is Job Creation by Startups?

The majority of the employment startups generate remains as new firms age, creating a lasting impact on the economy, according to this study.

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Michael Horrell and Robert Litan
Ewing Marion Kauffman Foundation

Abstract:

We analyze a Business Dynamics Statistics (BDS) dataset broken out by firm age to determine how total employment in startups changes as startups age. Conventional thinking on employment from startups is that many of the new jobs created by startups evaporate over the course of just a few years as firms exit the market. By tracking cohorts of firms started from 1977–2000, we find this to not be the case. While many firms exit over the life of each cohort (destroying jobs), other firms also grow (creating jobs). This growth in employment partially balances out the jobs lost by closing and shrinking firms. We also look at how recessions affect employment in these cohorts of firms. We find that starting a firm during a recession does not affect employment levels five years later, but cohorts of firms exposed to prolonged recessions did experience significantly lower employment levels.

Key Findings

  • Cohorts of firms started each year retain, on average, 80 percent of their initial total employment to age five.
  • Older cohorts of firms exhibit increasingly higher employment retention rates over five years, but these rates are not substantially higher than those of new startup cohorts.
  • Cohorts that start during a recession hire fewer people in the first few years following their birth, but they catch back up to the same levels of employment at age five.
  • Prolonged recessions, on the other hand, appear to lower employment among cohorts. Cohorts at age five that had survived through portions of three recession years had roughly 10 percent less employment (as compared to their startup years) than cohorts of firms that encountered no recessions in their first five years.

The Importance of Startups in Job Creation and Job Destruction

This study finds that net job growth occurs in the U.S. economy only through startup firms.

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Summary:

It’s well understood that existing companies of all sizes constantly create—and destroy—jobs. Conventional wisdom, then, might suppose that annual net job gain is positive at these companies. This study, however, shows that this rarely is the case. In fact, net job growth occurs in the U.S. economy only through startup firms.

The study bases its findings on the Business Dynamics Statistics, a U.S. government dataset compiled by the U.S. Census Bureau. The BDS series tracks the annual number of new businesses (startups and new locations) from 1977 to 2005, and defines startups as firms younger than one year old.

The study reveals that, both on average and for all but seven years between 1977 and 2005, existing firms are net job destroyers, losing 1 million jobs net combined per year. By contrast, in their first year, new firms add an average of 3 million jobs.

Further, the study shows, job growth patterns at both startups and existing firms are pro-cyclical, although existing firms have much more cyclical variance. Most notably, during recessionary years, job creation at startups remains stable, while net job losses at existing firms are highly sensitive to the business cycle.

Because startups that develop organically are almost solely the drivers of job growth, job-creation policies aimed at luring larger, established employers will inevitably fail, said the study’s author, Tim Kane, Kauffman Foundation senior fellow in Research and Policy. Such city and state policies are doomed not only because they are zero-sum, but because they are based in unrealistic employment growth models.

And it’s not just net job creation that startups dominate. While older firms lose more jobs than they create, those gross flows decline as firms age. On average, one-year-old firms create nearly one million jobs, while ten-year-old firms generate 300,000. The notion that firms bulk up as they age is, in the aggregate, not supported by data.

Neutralism and Entrepreneurship: The Structural Dynamics of Startups, Young Firms and Job Creation

Patterns of firm formation and survival help explain the extraordinary job creation by startups.

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Abstract:

An increasing number of studies and reports have shown that new and young companies account for most net job creation in the United States. This empirically documented reality, however, is not exclusively a result of new and young companies being particularly prolific or idiosyncratically superior to other firms.

Indeed, concepts such as job creation and entrepreneurship increasingly are conflated with young and small firms. Yet the age breakdown of job creation is partly a reflection of the dynamics of firm accumulation—how firms enter and exit and survive over a period of time. In any given year in the U.S. economy, new and young companies represent a plurality of all firms in the economy. That is, they make up the largest bloc of firms by age category, meaning their considerable job creation record is partly structural. This does not mitigate the contribution of these companies to job creation, but that contribution must be seen in the proper structural context.

An Overview of the Kauffman Firm Survey 2004–2008

Results from the 2004–2008 Data showed the impact of the economic crisis.

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Alicia Robb
E.J. Reedy
Janice Ballou
David DesRoches
Frank Potter
Zhanyun Zhao

Executive Summary

Although entrepreneurial activity is an important part of a capitalist economy, data about U.S. businesses in their early years of operation have been extremely limited. Only recently has it become apparent what important contributions new and young businesses make to job creation and innovation activities. As part of an effort to understand the dynamics of new businesses in the United States, the Ewing Marion Kauffman Foundation (the Foundation) sponsored the Kauffman Firm Survey (KFS), a panel study of new businesses founded in 2004 that have been tracked annually and will continue to be tracked through 2011. Tracking businesses over time allows us to follow business evolutions that would not be apparent in cross-sectional snapshots, the more typical collection method. The KFS dataset provides researchers with a unique opportunity to study a panel of new businesses from startup to sustainability (or exit), with longitudinal data centering on topics such as how businesses are financed; the products, services, and innovations these businesses possess and develop in their early years of existence; and the characteristics of those who own and operate them.

Results. The current data provide an understanding of how businesses are organized and operate in their first five years of existence (2004 through 2008) and provide some indicators of survival and growth. Other measures describe the characteristics of the panel, such as the extent to which these businesses are involved in innovative activities. A series of tables gives a broad overview of the business and owner characteristics and firm survival over the period, and provide some new information available in the Third Follow-up Survey.

Highlights include:

  • Like all firms, young businesses are seeing major impacts on their business operations from the economic crisis.
  • The most challenging problem faced by young businesses in 2008 was slow or lost sales. The second-most-challenging problem was the unpredictability of business conditions.
  • Nearly 80 percent of respondents said they were somewhat affected or affected a lot by the recent economic crisis.
  • External debt markets became even more important for young firms in 2008.
  • In the first year of operation, external debt markets provided the single largest source of financing. The new firms injected about $80,000 on average into their new ventures during the first year of operation. Outsider debt (bank loans, credit cards, credit lines, etc.) made up more than $32,000 of that total and was the single largest funding source.
  • Four years later, in 2008, surviving firms injected another $78,000 into their businesses with the amount of financial capital raised from outside credit markets increasing to $52,000. Thus, the importance of external debt markets on average continues to rise as firms survive and grow in their early years.
  • Of those firms that applied for new bank credit or a renewal of a line of credit in 2008, nearly one-third had their applications sometimes or always denied. The most common reasons for denial were insufficient collateral and poor personal credit history. In addition, a similar number of respondents as last year indicated that they didn’t apply for funding at some point when they needed credit because they feared their applications would be denied (18 percent).
  • By 2008, about 27 percent of firms that started in 2004 had permanently closed, 5 percent were sold or merged, and another 1 percent temporarily were not operating. The overall survival rate for the 2004 startups was 67.6 percent by the end of 2008, compared to 73.4 percent for year-end 2007.
  • Firms surviving through 2008 were much more likely than firms that exited over the period to have primary owners older than age 45. Previous industry experience and startup experience had less impact on firm survival prospects than owner age did.
  • While about 40 percent of firms had employees in 2004, by 2008 about 55.6 percent of surviving firms had employees. Surviving firms with employees, which are now four years old, increased average employment from 4.6 employees in 2004 to 6.7 employees in 2008. Thus, surviving firms were growing over this period.
  • By 2008, about 53 percent of firms had revenues greater than $25,000, compared with just 31 percent in 2004, and about 21 percent of firms had more than $100,000 in assets in 2004, compared with 33.2 percent of surviving firms in 2008.
  • Half of firms made investments in intangible assets in 2008, compared with just 14 percent of firms investing in research and development (R&D). Intangible asset spending averaged $28,000 in 2008, while average R&D spending was more than $54,000. High-tech firms are much more likely to have patents, copyrights, or trademarks. R&D investment and investment in intangible assets also were much higher for high-tech firms than for non-tech firms in 2008.
  • While the high-tech sector comprises only 5.6 percent of the firms, these firms are more likely to have employees and are larger in terms of sales and assets than non-tech firms are. They have a significantly higher four-year survival rate of 91 percent, versus 61 percent for non-high-tech firms.

Further analysis is available in papers that are posted to the KFS section of the Ewing Marion Kauffman Foundation Web site as they are completed (http://www.kauffman.org/kfs/).

Data Availability. The Kauffman Firm Survey is a research dataset accessible to scholars around the globe. The public-use microdata file for the Kauffman Firm Survey, which contains data from the Baseline, First, Second, Third, and Fourth Follow-up Surveys, is available at http://www.kauffman.org/kfs/. The dataset can be downloaded in SAS, STATA, or SPSS. Researchers wishing to access a more detailed data file and to engage with a community of researchers in analysis of the KFS should consider applying for access to the University of Chicago NORC Data Enclave. The NORC Data Enclave provides secure remote access to the KFS confidential microdata file, which contains more detail regarding industry codes, geographical codes (zip code, metropolitan statistical area, and state), firm credit scores, and many additional continuous variables (in addition to categorical variables). The KFS confidential microdata may only be accessed through the NORC Data Enclave. Details on applying can be found on the KFS Web site: http://www.kauffman.org/kfs.

KFS Design. The study created the panel by using a random sample from the Dun & Bradstreet (D&B) database list of new businesses started in 2004. In response to the Foundation’s interest in understanding the dynamics of high-technology businesses, the KFS oversampled these businesses based on the intensity of research and development employment in the businesses’ primary industries.

Mathematica Policy Research, Inc., conducted extensive questionnaire design activities to establish consistent definitions of what constituted a new business and the start of business operations, and to investigate the most efficient methods for collecting these data. The KFS sought to create a panel that included new businesses created by a person or team of people, purchases of existing businesses by a new ownership team, and purchases of franchises. To this end, the KFS excluded D&B records for businesses that were wholly owned subsidiaries of existing businesses, businesses inherited from someone else, and not-for-profit organizations. Also, previous research on new businesses has reported variability in how business founders perceive when their businesses started operations. Therefore, a series of questions was asked about indicators of business activity and whether these were conducted for the first time in the reference year (2004). These indicators included:

  • Payment of state unemployment (UI) taxes
  • Payment of Federal Insurance Contributions Act (FICA) taxes
  • Presence of a legal status for the business
  • Use of an Employer Identification Number (EIN)
  • Use of Schedule C to report business income on a personal tax return

To be “eligible” for the KFS, at least one of these activities had to have been performed in 2004 and none performed in a prior year.

The questionnaire covered a variety of topics, including business characteristics, strategy and innovation, business structure and benefits, financing, and demographics of the principals.

Data Collection Methodology. The Baseline Survey was conducted between July 2005 and July 2006. Interviews were completed with principals of 4,928 businesses that started operations in 2004, which translates to a 43 percent response rate when the sampling weights are applied. A self-administered Web survey and Computer-Assisted Telephone Interviewing (CATI) were used for the data collection, and KFS respondents were paid $50 to complete the interview. CATI completes accounted for 3,781 (77 percent) and Web completes accounted for 1,147 (23 percent) of the total interviews. The results across sampling strata show that 2,034 interviews were completed in the two high-technology strata (See Appendix A for more information about the sampling strata), and the remaining 2,894 interviews were completed among non-high-tech businesses.

The sample for the First Follow-Up Survey consisted of the 4,928 businesses that completed the Baseline Survey. The First Follow-Up was conducted between June 2006 and January 2007, and 3,998 interviews were completed, which translates to an 89 percent response rate after adjusting for the sample weights. During the First Follow-Up, a significantly larger percentage of interviews were completed through the Web survey (2,366 or 59 percent) than in the Baseline, with CATI completes accounting for 41 percent (1,632 interviews).

Data collection on the Second Follow-Up Survey closely mirrored that of the First Follow-Up. Data collection began on May 31, 2007, and concluded on December 1, 2007. Overall, the study continued to be successful in retaining panel businesses, achieving a response rate of 84 percent (weighted). There was a slight increase in the percentage of respondents who completed the Web survey (63 percent in the Second Follow-Up compared to 59 percent in the First Follow-Up). Because the Second Follow-Up Survey was the third annual survey in which KFS panel members were asked to participate, KFS respondents usually remembered the previous surveys and required little persuasion. Nonetheless, there were some refusals, which necessitated a refusal conversion effort. Of the 4,523 cases in the Second Follow-Up, 404 initially refused, of which 66, or 16 percent, were converted and completed the questionnaire.

The data collection for the Third Follow-Up began on June 24, 2008, and concluded on December 23, 2008. About two-thirds of the 2,915 respondents chose to answer the survey by Web, while about one-third answered by CATI. A 78 percent response rate (unweighted) was achieved. Several new questions were added on sources of comparative advantage, credit applications and loan turndowns, predominant market for the firm’s products and/or services, international sales, and Internet sales.

The Fourth Follow-Up occurred in 2009 about 2008 business activities. Seventy-one percent of the 2,606 respondents chose to answer the survey by Web. An 82 percent response rate (unweighted) was achieved. Several new questions about the business owner, such as marriage status, net worth, and perceptions of change, were added, as were questions on current topics such as the national financial crisis and loan guarantees. Additional details of the study design are available in the introduction as well as the appendices.

Kauffman Index of Entrepreneurial Activity 2010 Report

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Executive Summary

In 2009, the number of people reporting entry into entrepreneurial activity in the United States reached its highest point over the last fourteen years. This increased rate of entrepreneurship was seen across most demographic categories, with the largest increases coming among older individuals and African-Americans. While the West continues to have a higher rate of entrepreneurship than other parts of the country do, it showed a sharp decline in 2008. These trends and many more are discussed here in the Kauffman Index of Entrepreneurial Activity, the leading indicator of new business creation in the United States.

Capturing new business owners in their first month of significant business activity, this measure provides the earliest documentation of new business development across the country. The percentage of the adult, non-business-owner population that starts a business each month is measured using data from the Current Population Survey (CPS). In addition to this overall rate of entrepreneurial activity, separate estimates for specific demographic groups, states, and select metropolitan statistical areas (MSAs) are presented. The Index provides the only national measure of business creation by specific demographic groups.

New 2009 data allow for an update to previous reports, with consideration of trends in the rates of entrepreneurial activity over the fourteen-year period between 1996 and 2009. The Kauffman Index reveals important shifts in the national level of entrepreneurial activity and shifts in the demographic and geographic composition of new entrepreneurs across the country. Key findings for 2009 include:

  • In 2009, 0.34 percent of the adult population (or 340 out of 100,000 adults) created a new business each month, representing approximately 558,000 new businesses per month. The 2009 entrepreneurial activity rate represents an increase over the 2008 rate of 0.32 percent and represents the highest level over the past decade and a half.
  • Overall, men are substantially more likely to start businesses each month than are women. The entrepreneurial activity rate for men increased slightly from 0.42 percent in 2007 to 0.43 percent in 2008. The Kauffman Index for women also increased slightly, from 0.24 percent to 0.25 percent.
  • The entrepreneurial activity rate among African-Americans increased from 0.22 percent in 2008 to 0.27 percent in 2009, reaching the highest level over the past decade and a half.
  • The Latino entrepreneurial activity rate decreased from 0.48 percent in 2008 to 0.46 percent in 2009, and the Asian entrepreneurial activity rate decreased from 0.35 percent in 2008 to 0.31 percent in 2009. The non-Latino white business-creation rate increased from 2008 to 2009 (0.31 percent to 0.33 percent).
  • The immigrant rate of entrepreneurial activity declined slightly from 0.53 percent in 2008 to 0.51 percent in 2009, but remained substantially higher than the native-born rate of 0.30 percent.
  • The oldest age group (ages fifty-five to sixty-four) experienced the second-largest increase in business-creation rates from 2008 to 2009, contributing to a two-year upward trend. Among this group, entrepreneurial activity rose from 0.36 percent to 0.40 percent. The age group thirty-five to forty-four also experienced a large increase in entrepreneurial activity from 2008 to 2009 (0.35 percent to 0.40 percent). The youngest age group (twenty to thirty-four) has a substantially lower entrepreneurship rate (0.24 percent).
  • Entrepreneurship rates increased the most for college-educated individuals (0.31 percent to 0.34 percent), and high school individuals (0.35 percent to 0.38 percent) in 2009.
  • The construction industry had the highest rate of entrepreneurial activity of all major industry groups in 2009 (1.55 percent). The second-highest rate of entrepreneurial activity was in the services industry (0.42 percent).
  • The entrepreneurial activity rate declined sharply in the West, from 0.42 percent in 2008 to 0.38 percent in 2009. Business creation rates increased in the Midwest and South, but the West continues to have the highest rates.
  • The states with the highest rates of entrepreneurial activity were Oklahoma (470 per 100,000 adults), Montana (470 per 100,000 adults), Arizona (460 per 100,000 adults), Texas (450 per 100,000 adults), and Idaho (450 per 100,000 adults). The states with the lowest rates of entrepreneurial activity were Mississippi (170 per 100,000 adults), Nebraska (200 per 100,000 adults), Pennsylvania (200 per 100,000 adults), Alabama (210 per 100,000 adults), and Minnesota (220 per 100,000 adults).
  • The states experiencing the largest increases in entrepreneurial activity rates over the past decade were Georgia (0.20 percentage points), Arizona (0.14 percentage points), Tennessee (0.13 percentage points), the District of Columbia (0.12 percentage points), and Massachusetts (0.10 percentage points). The states that experienced the largest decreases in their rates were New Mexico (-0.20 percentage points), Alaska (-0.15 percentage points), North Dakota (-0.12 percentage points), and Nebraska (-0.10 percentage points).
  • Among the fifteen largest MSAs in the United States, the highest entrepreneurial activity rate in 2009 was in Houston (0.63 percent). The large MSA with the lowest rate of entrepreneurial activity was Seattle (0.16 percent).

Facilitating the Commercialization of University Innovation

This paper examines an innovative new set of practices associated with the commercialization of university research developed and implemented at the University of North Carolina at Chapel Hill.

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The Carolina Express License Agreement

Joseph M. DeSimone
Chancellor’s Eminent Professor of Chemistry
University of North Carolina at Chapel Hill
William R. Kenan, Jr.
Distinguished Professor of Chemic
al Engineering
North Carolina State University
Lesa Mitchell
Vice President, Advancing Innovation
Ewing Marion Kauffman Foundation

Introduction

As the United States recovers from the most severe recession since the 1930s, efforts to boost economic growth assume paramount importance. This means not not only finding ways to spur rapid job creation but also advancing the country’s capacity for innovation. One way to do this is to encourage academic research enterprises to facilitate the transfer and spillover of scientific and technological research into commercial application. In particular, the research laboratories of our nation’s universities are unparalleled sources of dynamic new spin-off and startup companies. These fledgling enterprises in turn have the potential to become high-growth firms. Recent Kauffman Foundation research demonstrates that top-performing new companies are a fertile source of new jobs.

In the following paper, we examine an innovative new set of practices associated with the commercialization of university research developed and implemented at the University of North Carolina at Chapel Hill. New standard licensing agreements support and expedite firm formation as an outcome of universitybased intellectual property. A committee chaired by one of this paper’s authors developed the licensing agreement. The committee included entrepreneurial UNC faculty members with experience in firm birthing, colleagues from the Office of Technology Development (OTD), venture capitalists from the firm Intersouth Partners, and attorneys from a number of firms that have represented UNC startups. The outcome of these discussions produced the Carolina Express License Agreement.

Exploring Firm Formation: Why is the Number of New Firms Constant?

According to this study, new-business creation in the United States is remarkably constant over time.

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Recent entrepreneurship research has shed new light on how important new companies—firms less than five years old—are to economic growth, so the next question raised by economists and policymakers might be: How do we increase the number of firm formations? In a review of research into entrepreneurial orientation to help find answers, another important question has arisen: Why does the level of firm formation remain virtually consistent from year to year?

This paper, the second in the Kauffman Foundation Research Series on Firm Formation and Economic Growth, explores this question and makes the following key points:

  • Firm formation in the United States is remarkably constant over time, with the number of new companies varying little from year to year. This remains true despite sharp changes in economic conditions and markets, and longer-cycle changes in population and education. While existing entrepreneurship data may miss some numbers of new firms, this does not appear to explain the steady level of firm formation across time.
  • Such constancy possibly reflects the nature of the United States economy, employment churn, and demographics. The paper discusses each in detail, as well as entrepreneurial motivations, talent, and the so-called “opportunity recognition” model.
  • A steady level of firm formation implies that relatively few factors, such as entrepreneurship education and venture capital, influence the pace of startups, although these factors may help prevent a decline of new firms and may affect specific companies at the margins.
  • A closer look at the relatively unchanging number of new firms each year offers potential lessons for public policy, especially when considering the future of entrepreneurship after the Great Recession of 2007–2009.

This paper examines the implications of each of these points, such as possible reasons why firm formation is constant and what it means for the wider economy, why efforts to increase entrepreneurship have not had much effect on the level of firm formation, whether or not the volume of startups really matters to the economy, and how the recession has impacted firm formation.