Lessons for U.S. Metro Areas: Characteristics and Clustering of High-Tech Immigrant Entrepreneurs Share: Facebook LinkedIn Twitter Download the White Paper Lessons for U.S. Metro Areas: Characteristics of High-Tech Immigrant Entrepreneurs pdf An open and culturally diverse environment helps promote high-tech entrepreneurship among both immigrants and the U.S.-born, according to a new research report released today by the Ewing Marion Kauffman Foundation. Immigrant-owned businesses, the study shows, are more likely to locate in ethnically diverse metro areas that have high foreign-born populations. That’s important for metro areas hoping to attract and retain this fast-growing pool of high-impact founders. The study, “Lessons for U.S. Metro Areas: Characteristics and Clustering of High-Tech Immigrant Entrepreneurs,” also reports that regional labor markets with greater percentages of high-tech industries and greater numbers of college graduates and patents – all indicators of innovation – tend to attract other high-tech companies. Immigrants comprised 20 percent of the high-tech work force and 17.3 percent of high-tech entrepreneurs between 2007 and 2011, according to the study, which used the American Community Survey. This represents an increase of 13.7 percent and 13.5 percent, respectively, from 2000. In addition, between 2000 and 2011, the number of self-employed immigrants in high-tech industries increased by 64 percent, compared with 22.6 percent for U.S.-born. Immigrants’ impact on high-tech entrepreneurship varies according to country of origin, the study found. Since the beginning of the new century, immigrants from Colombia, China, India, Korea and Vietnam expanded the number of the self-employed in high-tech industries. On the other hand, the number of high-tech entrepreneurs from Iran, England, Mexico, Germany and Cuba stagnated. The paper’s authors, Cathy Yang Liu from Georgia State University, Gary Painter from the University of Southern California and Qingfang Wang from the University of North Carolina at Charlotte found that, compared to high-tech businesses owned by those who are U.S.-born, immigrant-owned high-tech businesses are more concentrated in industry categories such as semiconductor; other electronic components; magnetic and optical media; communications; audio/video equipment; and computer science-related sectors. The paper also points out that immigrant high-tech entrepreneurs are concentrated in a smaller number of metropolitan areas. Eighty percent of immigrant high-tech entrepreneurs operate in the largest 25 metropolitan areas, compared with 57 percent of their U.S.-born counterparts. The report lists the top 25 Metropolitan Statistical Areas by their share of high-tech entrepreneurs, immigrant and U.S. born. Not surprisingly, New York, Los Angeles and San Francisco metropolitan areas account for about a third of all immigrant high-tech entrepreneurs in the country in 2011. However, metros registering substantial growth over the last decade include Atlanta, Chicago, Fort Lauderdale, Houston, Miami, Riverside and Washington, D.C. Interestingly, the metros of Silicon Valley – San Francisco and San Jose – did not experience substantial growth.
Missouri Charter Schools and Teacher Pension Plans: How Well Do Existing Pension Plans Serve Charter and Urban Teachers Share: Facebook LinkedIn Twitter Download the Report and Erratum Missouri Charter Schools and Teacher Pension Plans: How Well Do Existing Pension Plans Serve Charter and Urban Teachers? pdf Erratum to Missouri Charter Schools and Teacher Pension Plans: How Well Do Existing Pension Plans Server Charter and Urban Teachers? pdf Note: An erratum to this report was published August 27, 2014. The initial report was inaccurate in stating that there is not reciprocity between the Missouri Public School Retirement System (PSRS) and the Kansas City Public School Retirement System (KC).The correction also examines rigidities in the transfer agreement that cause educators to face significant pension penalties in moving districts. For decades, education has been a political football. From differing opinions over funding levels and diversity, to arguments over what to teach and how to teach it, education debates are often proxies for ideological disputes. While these arguments still rage, there is an emerging consensus that the central actor in improving educational outcomes is the teacher. And, therefore, the locus of change and reform is the teacher. To some, this may sound rather obvious: haven’t teachers always been central to education? Yes, but ongoing education debates often treat teachers as incidental. With teachers at the center, we can focus on public policies that support the entire system of teaching—from new teacher training to working conditions to ongoing professional development to a facilitative labor market. Organizations across the political spectrum are working on these issues, and all recognize that there is no “one solution” that will address the challenges at once. Each dimension is nuanced and embedded in years of institutional tradition. The Ewing Marion Kauffman Foundation focuses much of its education work on urban school districts, where the general challenges of American education are especially acute. Urban districts are a crucible of many teacher-related issues. Our experience with the salient characteristics of urban education systems—frequent teacher turnover in the early years, difficulties recruiting teachers and leaders, performance gaps, funding constraints, and so on—led us to identify the teacher labor market as a potential point of change in enhancing support for teachers. One important dimension of the teacher labor market is pensions; this, too, has sometimes been a source of contention among teachers, administrators, and policymakers. But those arguments (usually concerning money) miss broader points. How do we design teacher pension plans that align with labor market realities and the interests of all teachers? Can alternative designs help address challenges of recruitment and retention, especially for urban districts? These are the questions that Podgursky, Koedel, Ni, and Xiang explore in this paper with respect to Missouri. It turns out that Missouri is a good place to examine these questions because of its tripartite educator retirement system. Uniquely, the state has three separate pension systems: the Kansas City Public School Retirement System (KC), the Public School Retirement System of the City of Saint Louis (STL), and the Missouri Public School Retirement System (PSRS). The KC and STL retirement systems cover employees of district and charter schools only in the two respective city school districts. The statewide system, PSRS, covers everyone else. Because of this fragmented structure, with the two urban districts carved out separately, Missouri is a microcosm of larger national issues concerning teacher pension systems—particularly the ability of teachers to move between systems. A well-functioning labor market improves the “match quality” between teachers and schools, which helps teachers be more effective and improves school quality. But, a well-functioning labor market also requires, among other things, transportability and reciprocity, which are absent or have significant limitations between the three Missouri systems. This and other features of the urban district pension systems are burdens on teachers and schools. The absence and rigidness of transfer agreements among the systems exacerbates the challenges already facing the KC and STL urban districts. For example, state data show that, within eight years of starting employment in either the KC or STL systems, between 80 percent and 90 percent of teachers have left the systems. Only a small fraction of teachers make up the “pension wealth mountain” that the authors describe. The urban districts also face pension-related challenges in recruiting school leaders. The rising share of charter schools in KC and STL (now between 30 percent and 40 percent of educators) underscores the importance of exploring issues related to pension systems and ways to design systems that ensure retirement security for teachers and buttress the ability of urban districts to recruit and retain teachers and leaders. At the heart of this research paper and the Kauffman Foundation’s support for it is the question regarding how well Missouri’s system of retirement plans serves the educators working within those plans. We also are interested to know if there are ways those plans, or different plans, could serve as a more effective tool to recruit and retain teachers in the state’s two urban centers. Teachers deserve supports that make the fruits of their labors as secure and robust as possible, while also allowing them to work in different locations without fear of losing benefits for which they have worked so diligently. Students deserve the best possible education professionals in their schools, and we hope this research provides insight into ways Missouri can better make that possible.
In Search of a Second Act: The Challenges and Advantages of Senior Entrepreneurship Share: Facebook LinkedIn Twitter Testimony Before the U.S. Senate Special Committee on Aging & the Senate Committee on Small Business and Entrepreneurship Contrary to popular perception, entrepreneurship is not exclusive to the young and hip. With that message, Kauffman Foundation Vice President of Research and Policy, Dane Stangler, provided testimony to the U.S. Senate Special Committee on Aging & the Senate Committee on Small Business and Entrepreneurship on how policymakers can foster senior entrepreneurship and why it is important to the U.S. economy. Testimony of Dane Stangler Vice President, Research and Policy Ewing Marion Kauffman Foundation Before the U.S. Senate Special Committee on Aging & the Senate Committee on Small Business and Entrepreneurship In Search of a Second Act: The Challenges and Advantages of Senior Entrepreneurship February 12, 2014 Chairman Nelson, Chair Landrieu, Ranking Member Collins, Ranking Member Risch, and members of the Aging and Small Business and Entrepreneurship Committees, thank you for the opportunity to present data gathered by the Ewing Marion Kauffman Foundation on senior entrepreneurship. Founded by late entrepreneur and philanthropist Ewing Marion Kauffman, the Kauffman Foundation is a private, nonpartisan foundation based in Kansas City, Missouri that aims to foster economic independence by advancing educational achievement and entrepreneurial success. At the Kauffman Foundation, we believe in the power of entrepreneurship to not only change individual lives, but to also create economic opportunities for many others in society. With the goal of creating new knowledge about entrepreneurship, the Kauffman Foundation conducts and supports research that informs policymakers and the public about pro-entrepreneurship policies at all levels of government. Our research contributes to a more in-depth understanding of what drives innovation and economic growth in an entrepreneurial world. Contrary to popular perception, entrepreneurship is not exclusive to the young and hip. Entrepreneurs of all ages start businesses and create economic opportunity for themselves and others. Last year, for example, businesses started by those ages 55 to 64 accounted for nearly one-quarter of all new businesses started. That share has risen from 14 percent in 1996, according to the Kauffman Index of Entrepreneurial Activity, which captures business owners in their first month of significant business activity. In the context of America’s aging population, an increasing share of entrepreneurship among this population is perhaps not surprising. What might be more startling to many observers is that Americans in the 55-64 age group start new businesses at a higher rate than those in their twenties and thirties. This has been true, by the way, in every single year from 1996 to 2013. While senior entrepreneurs make up a sizeable portion of all entrepreneurs and tend to start businesses at a rate comparable to or higher than younger entrepreneurs, there are possibly some reasons to temper our enthusiasm about this phenomenon. First, we are unsure of the types of businesses being founded by older entrepreneurs or their hiring practices — more cynical observers say that this group only starts consulting companies or use self-employment for supplemental income. This is undoubtedly true for some share of older entrepreneurs. Yet other evidence indicates that we find founders of technology companies in their fifties and sixties as well: one study found more tech founders over age 50 than under age 30.4 Of more concern perhaps is the lingering effect of the Great Recession and the decimation of retirement plans and housing wealth. To the extent this damage fell on Americans over age 55, self-employment may be seen as a way to recover nest egg losses. Finally, with concern about Americans over age 55 permanently leaving the labor force after the recession, it is possible that older entrepreneurship rates could be suppressed. Nevertheless, there are more reasons for optimism than pessimism about entrepreneurship among older Americans. First, senior entrepreneurs are likely to have greater experience than younger entrepreneurs. That experience, whether professionally or personally, can prove valuable when starting a new business. Secondly, perhaps paradoxically, senior entrepreneurs may have fewer concerns about setting up a business. In their paper on entrepreneurs over the age of 50 in the United Kingdom, Ron Botham and Andrew Graves found that older entrepreneurs were “less likely to worry about risks, experience, or family life than younger founders.” Third, despite the effects of the recession, senior entrepreneurs may be more financially secure than younger entrepreneurs and may have an alternative source of income — either from retirement savings, a pension, or Social Security. This added financial security can make the financial risks of starting a business less salient. Finally, we might expect a higher preponderance of serial entrepreneurs among those in their fifties and sixties, which could mean greater success rates. A 2012 Kauffman Foundation and LegalZoom survey of 1,400 business owners who incorporated their business through LegalZoom in 2012 found two-thirds of respondents over age 60 had previously started a company and ten percent of these entrepreneurs had started 5 prior companies. Research suggests that there are several ways policymakers could support this very important phenomenon of older entrepreneurship. Lower barriers to entry in general, for example, would make business creation easier. Licensing barriers in several sectors — which exist mostly at the state and local level — also suppress business creation. The complexity — though not necessarily the level — of taxes can also act as a barrier to entrepreneurship. These, of course, apply to entrepreneurs of all ages. For senior entrepreneurship, flexible labor markets are especially important. The idea of spending forty years at one job and retiring with a gold watch is quickly fading in the United States. Even when Americans retire at age 65, they can expect to live healthily for another two or three decades. Moving easily between self-employment, wage-and-salary employment, and entrepreneurship requires flexible labor markets. This may be especially important for senior entrepreneurship as research has shown that senior entrepreneurs are much more likely to start a business if moving from a job. In addition, fostering more senior entrepreneurship as the American population ages will require careful attention to specific sectors in order to foster innovation. n particular, we will likely need more financial innovation to support continuously changing forms of entrepreneurship. Finally, policymakers can foster senior entrepreneurship by encouraging intergenerational networks where entrepreneurs of different ages can interact and learn. The Kauffman Foundation started a new entrepreneurial support program called 1 Million Cups in Kansas City that has spread to more than two dozen cities across the United States. Each week, the 1 Million Cups program offers local entrepreneurs an opportunity to present their startups to a diverse audience of mentors, advisors, and entrepreneurs. Presenters prepare a short educational presentation and engage in 20 minutes of feedback and questioning after they present. Entrepreneurs gain insight into possible ways they can improve their businesses, gather realtime feedback, connect with a community that truly cares about their progress, and walk away feeling like they have advanced their business. These community gatherings provide opportunities for individuals of all ages to connect around entrepreneurship. In conclusion, older Americans are active entrepreneurs whose new businesses provide self-employment and employment opportunities to others. As the American population ages, we should expect a greater share of entrepreneurs to be seniors. Policymakers can support these “third age” or “encore” entrepreneurs by pursuing policies that lower barriers to entrepreneurial entry, maintain flexible labor markets, and encourage intergenerational interaction. Thank you, again, for the opportunity to testify.
Declining Business Dynamism in the U.S. High-Technology Sector This white paper shows sustained declines in business dynamism across a wide swath of the U.S. economy, including the high-tech sector that has been critical for sparking economic growth in recent decades. Share: Facebook LinkedIn Twitter Download the Report Declining Business Dynamism in the U.S. High-Technology Sector pdf Abstract The U.S. economy is very dynamic—with firms entering, exiting, expanding, or contracting at all times. More competitive firms grow and replace less-competitive ones. This dynamic process is an important source of productivity growth and sustained economic prosperity in modern economies. New and young firms play an outsized role in this productivity—enhancing dynamic process, and in net job creation. But, recent trends point to sustained declines in business dynamism and in entrepreneurship across a broad range of sectors in the U.S. economy. While the causes and implications of this development are still being uncovered, it may suggest a lower growth economy and standards of living than otherwise would have been. We examine how these trends apply to the U.S. high-tech sector—defined here as the group of industries with very high shares of workers in the STEM occupations of science, technology, engineering, and math. Our findings show that the recently documented secular declines in business dynamism that occurred broadly across the U.S. economy during the last couple of decades also occurred in the high-tech sector in the post-2000 period. As part of this decline in dynamism, we find indicators of a slowdown in entrepreneurship in the high-tech sector in the post-2000 period. This slowdown in the high-tech sector may be especially problematic for all the reasons stated above. High-tech firms also play an outsized role in income, employment, and productivity growth overall and are generally focused on the types of cutting-edge technologies that can drive sustained economic growth. This sector typically is viewed as very entrepreneurial, but we document a pronounced slowdown in such activity in the post-2000 period. Introduction Business churning is an important part of economic activity. Some firms are born while others fail, and some companies expand while others contract. New and superior ideas, processes, and goods replace obsolete ones in a dynamic process of “creative destruction.”1 Labor markets reflect that churning as some jobs are created while others are destroyed, and some workers move into new roles as others seek to replace them.2 Though costly for some individual workers or firms in the short-term, this process contributes substantially to productivity growth overall as labor and capital are more efficiently allocated across the economy.3 This makes the process of business- and labor-market churning indispensable because the resulting productivity gains help drive sustained economic growth. A number of signs point to a secular decline in U.S. business dynamism, which goes far beyond the more recent effects of the Great Recession.4 For example, the rate of new firm formation—a key element of business dynamism and new job creation—has been declining steadily for at least the last three decades. Job reallocation—the process that moves workers away from contracting or closing businesses and toward expanding or new firms—also has been declining over the same period. We contribute to the understanding of the secular decline in business dynamism in the United States by examining how these trends apply to the innovative high-tech sector—defined as the group of industries with very high shares of workers in the STEM fields of science, technology, engineering, and math (see Appendix A). Despite its relatively small size⌴representing just 4.1 percent of total private-sector firms in 2011⌴the high-tech sector packs a lot of economic punch. Aside from the obvious productivity gains across the U.S. economy that are directly attributable to the adoption of high-tech goods and services, the high-tech sector itself is a key contributor to income generation, job creation, and productivity growth.5 Because of this, a slowdown in high-tech entrepreneurial activity might have disproportionate effects on long-term economic growth overall. Job Creation and Destruction A standard approach to understanding business dynamism is to examine the job flows associated with this process during a given period of time. Figure 1 shows annual job creation and destruction rates for the high-tech sector between 1978 and 2011 using the Business Dynamics Statistics (BDS) of the U.S. Census Bureau.6 Because these data are based on annual snapshots of U.S. businesses over time, annual job creation reflects a net addition of employment at a particular business through one of two channels⌴the expansion of employment at an existing business establishment or the birth of a new one in a particular year. Job destruction reflects a net loss of employment—when an existing business either contracts employment or closes its doors. Fig. 1: Gross Job Creation and Destruction Rates in High-Tech Sector (1978–2011) High-Tech Source: U.S. Census Bureau, BDS and Special Tabulation; authors’ calculations Note: Trends are calculated by applying a Hodrick-Prescott filter with a multiplier of 400 As Figure 1 shows, the rate of both job creation and destruction in the high-tech sector were elevated in each year. The high-tech boom in the second half of the 1990s is evident, with a high pace of job creation and a slightly increasing rate of job destruction during this period. The spike in job destruction in the March 2001 to March 2002 period is associated with the well-known dot-com bust. Of particular interest for the current analysis is the slowdown in the overall pace of job creation and destruction in the post-2002 period. This slowdown is evident in the declining trends of both job creation and job destruction from about 2004 onward. The drop in both gross job creation and net job creation has been especially pronounced in the wake of the Great Recession. To compare the patterns for the high-tech sector to the private sector as a whole, a summary measure of economic dynamism is used: the job reallocation rate. The latter measures the sum of job creation and destruction rates in a given year, providing an integrated view of business dynamism. Here, we focus on trend rates rather than the actual rates themselves. Fig. 2: Trends in Job Reallocation Rates: High-Tech vs. Private Sector (1978–2011) Source: U.S. Census Bureau, BDS and Special Tabulation; authors’ calculations Note: Trends are calculated by applying a Hodrick-Prescott filter with a multiplier of 400 As Figure 2 shows, job reallocation in the entire private sector has been on a sharp and steady trend decline for the last few decades, while the high-tech sector exhibited a trend increase in the pace of reallocation until about 2002. However, since 2002 there has been a sharp trend decline in high-tech sector job reallocation that has even exceeded the pace of the decline in the overall economy. So, interestingly, the high-tech sector bucked the national trend by exhibiting rising dynamism until 2002—but even it has exhibited a trend decline since 2002. Entrepreneurship Rates A key player in the process of creative destruction and business dynamism is the entrepreneurial firm, which is measured here by firm age—in particular, new and young firms (those aged five years or younger). Previous research has firmly established that these businesses play a central role in productivity gains and employment growth.7 While mature firms are responsible for the majority of employment levels (static), it is new and young firms that make disproportionately large contributions to net new jobs (dynamic) overall.8 A recent Engine-Kauffman Foundation report analyzed firm formation and job creation in the high-tech sector, extending the existing research to this innovation-driven segment of the economy.9 It found that the high-tech sector has produced an outsized share of entrepreneurship and job creation during the last few decades, and has been spreading throughout the country.10 Even among job-creating young firms, surviving young high-tech businesses add jobs at a rate twice that of all surviving young firms, and the rate of job creation is so robust that it offsets losses from early-stage failures—something that is not true for young firms as a whole.11 In short, firms aged five or younger are key drivers of new job creation, a fact that is especially true in high-tech. Sustaining a robust rate of net new job creation requires a constant supply of firm births each year. Figure 3 shows the number of new and young firms (aged five years or younger) annually between 1990 and 2011, comparing the high-tech sector against all private-sector firms. Fig. 3: Young Firms (aged five years or younger) by Sector (1990–2011) Source: U.S. Census Bureau, BDS and Special Tabulation; authors’ calculations Several patterns stand out that remind us of the particular nature of the high-tech sector and some of its idiosyncrasies vis-à-vis the rest of the economy. First, the number of young high-tech firms increased considerably during this period⌴more than doubling between 1990 and 2007, to 97,836 from 45,959. For the private sector as a whole, young firms held steady throughout much of this period, even though the overall number of firms was growing substantially at the same time. The 1990s saw a particularly sharp rise in high-tech entrepreneurship coinciding with wide adoption of the Web and speculation around Internet-based companies (the dot-coms). This period of growth ends with the collapse of the dot-com bubble and instigates a steep decline in high-tech entrepreneurship in the late 1990s and early 2000s. From about 2002, the number of high-tech young firms continues to decline, while there is a modest increase in the number of young firms overall. The impact of the Great Recession on entrepreneurship is evident after 2007, with sharp declines in the number of young businesses both in the high-tech sector and in the economy as a whole. The number of young high-tech firms fell to 79,034 in 2011, marking a 19.2 percent drop from 2007. By contrast, the number of young firms for the entire private sector fell by 18.3 percent during the same period. Looking at the absolute number of new and young firms can help us identify relevant trends and inflection points affecting entrepreneurship. However, it does little to help us understand the context in which these patterns take place. A more relevant statistic in this regard is the entrepreneurship rate, which tells us the relative importance young firms have in a sector. Fig. 4: Young Firms (aged five years or younger) as a Share of Total Firms by Sector (1990–2011) Source: U.S. Census Bureau, BDS and Special Tabulation; authors’ calculations Figure 4 shows entrepreneurship rates in the high-tech and the private sector as a whole. The entrepreneurship rate is defined as the number of startups and young firms (up to five years old) over the total number of firms. The entrepreneurship rate in the high-tech sector has declined significantly despite the actual increase in absolute numbers during the same period. The high-tech entrepreneurship rate fell from a high of nearly 60 percent in 1990 to a low of 38 percent by 2011. However, the decline has not been monotonic, with a rise in the entrepreneurship rate in the second half of the 1990s, which was followed by the dot-com bust. Perhaps even more relevant is the continued decline in the entrepreneurship rate in the post-2002 period. The latter occurs at a pace that even exceeds the decline in entrepreneurship for the private sector as a whole during the same period.12 Why entrepreneurial activity has been so anemic in the high-tech sector post-2002 is an open question.13 The overall economy has been exhibiting a declining trend in entrepreneurial activity over a much longer period, but now, even the highly dynamic and entrepreneurial high-tech sector is becoming less so. Conclusion In the post-2000 period, the high-tech sector is experiencing a process of economic activity consolidation, away from young firms and into more mature firms. The high-tech sector looked different than the rest of the private economy did during the 1990s, when the share of young firms was declining in the overall economy but rising in high-tech. In the early 2000s, entrepreneurial activity in the high-tech sector began declining sharply during what is well-known as the dot-com bust. Less well known is that the share of young firms in the high-tech sector has exhibited a more pronounced secular decline in the post-2002 period than in the rest of the economy. Consistent with that pattern, we have found that the pace of business dynamism, as measured by the pace of job reallocation, has declined in the high-tech sector in the post-2002 period at a pace that exceeds that of the overall economy. Empirical evidence suggests a link between business dynamism, innovation, and productivity growth. In this regard, the findings here point to the possibility of a slowdown in productivity and economic growth in the high-tech sector in the last decade. The slowdown we find for the high-tech sector might be an even larger source of concern than that for the overall economy, since young high-tech firms may be more important for innovation and new job creation than their non-high-tech counterparts are. Appendix A: Defining High-Tech According to a Bureau of Labor Statistics study published in 2005 that followed an interagency seminar aimed at classifying high-tech industries, a high-tech industry is defined by the presence of four factors: a high proportion of scientists, engineers, and technicians; a high proportion of R&D employment; production of high-tech products, as specified on a Census Bureau list of advanced-technology products; and the use of high-tech production methods, including intense use of high-tech capital goods and services in the production process.14 The study also concluded that because of “data and conceptual problems,” the intensity of “science, engineering, and technician” employment would be the basis for identifying high-tech industries. Seventy-six “technology-oriented occupations” were used to conduct the employment intensity analysis. A condensed list is outlined in Table 1, but broadly speaking, these occupations coalesce around three groups—computer and math scientists; engineers, drafters and surveyors; and physical and life scientists.15 Table 1: Technology-Oriented Occupations Source: Bureau of Labor Statistics After this group of occupations was identified, an intensity analysis was conducted to determine which industries contained large shares of these technology-oriented workers. Of the more than 300 industries at the level of granularity used, the fourteen shown in Table 2 had the highest concentrations of technology-oriented workers. Each of these fourteen “Level-1” industries had concentrations of high-tech employment at least five times the average across industries.16 Table 2: High-Technology Industries Source: Bureau of Labor Statistics This report uses the method described above to define the high-tech sector of the U.S. economy. Checks were made to ensure that the identifying conditions held in the latest available data, and crosswalks were performed to account for changes in industry and occupation classifications over time. Though the Bureau of Labor Statistics report ultimately concluded that a wider group of industries could be considered high-tech, this report uses a more conservative approach by analyzing just the fourteen Level-1 industries with very high concentrations of technology-oriented workers in the STEM fields of science, technology, engineering, and math. 1 Schumpeter (1942), Capitalism, Socialism & Democracy (London and New York: Routledge, 1943), pages 81–86. 2 See Davis and Haltiwanger, “Gross Job Flows,” Handbook of Labor Economics, O. Ashenfelter and D. Card (ed.), 1999, for a review of the literature. 3 For a recent review of the empirical literature, see Syverson (2011), “What Determines Productivity?,” Journal of Economic Literature, 49(2): 326–65; Haltiwanger (2011), “Job Creation and Firm Dynamics in the U.S.,” Innovation Policy and the Economy, Volume 12, NBER. 4 Hathaway, Bell-Masterson, and Stangler (2013), “The Return of Business Creation,” KauffmanFoundation; Haltiwanger, Jarmin, and Miranda (2012), “Where Have All the Young Firms Gone?” Kauffman Foundation; Reedy and Litan (2011), “Starting Smaller; Staying Smaller: America’s Slow Leak in Job Creation,” Kauffman Foundation. 5 Hathaway (2012), “Technology Works: High-Tech Employment and Wages in the United States,” Bay Area Council Economic Institute; Spence and Hlatshwayo (2011), “The Evolving Structure of the American Economy and the Employment Challenge,” Comparative Economic Studies. 6 The BDS is a publicly available dataset available at https://www.census.gov/programs-surveys/bds.html/. For a description of the methodology used in its creation, see https://www.census.gov/programs-surveys/bds/documentation/methodology.html. The data for high-tech are not publicly available, but were prepared by the U.S. Census Bureau’s Center for Economic Studies as a Special Tabulation. 7 Dunne, Roberts, and Samuelson (1989), “Plant Turnover and Gross Employment Flows in the U.S. Manufacturing Sector,” Journal of Labor Economics; Davis and Haltiwanger (1999), “Gross Job Flows,” Handbook of Labor Economics; Foster, Haltiwanger, and Krizan (2001), “Aggregate Productivity Growth: Lessons from Microeconomic Evidence,” Studies in Income and Wealth; Foster, Haltiwanger, and Kirzan (2006), “Market Selection, Reallocation and Restructuring in the U.S. Retail Trade Sector in the 1990s,” The Review of Economics and Statistics; Haltiwanger, Jarmin, and Miranda (2013), “Who Creates Jobs? Small vs. Large vs. Young,” Review of Economics and Statistics. 8 Haltiwanger, Jarmin, and Miranda (2013), “Who Creates Jobs? Small vs. Large vs. Young,” Review of Economics and Statistics; Horrell and Litan (2010), “After Inception: How Enduring is Job Creation by Startups?,” Kauffman Foundation; Kane (2010), “The Importance of Startups in Job Creation and Job Destruction,” Kauffman Foundation; and Haltiwanger, Jarmin, and Miranda (2009), “Jobs Created from Business Startups in the United States,” Kauffman Foundation. 9 Hathaway (2013), “Tech Starts: High-Technology Business Formation and Job Creation in the United States,” Kauffman Foundation. Even among young, job-creating firms, young high-tech businesses add jobs at a rate twice that of all firms, and the rate of job creation is so robust that it offsets losses from early-stage failures—something that is not true for young firms as a whole. 10 For a more detailed discussion of the geographic dimensions of high-tech startups, see Stangler (2013), “Path-Dependent Startup Hubs, Comparing Metropolitan Performance: High-Tech and ICT Startup Density,” Kauffman Foundation. 11 Hathaway (2013), “Tech Starts: High-Technology Business Formation and Job Creation in the United States,” Kauffman Foundation. 12 The patterns for the overall economy are consistent with recent findings for the whole economy by Decker, Haltiwanger, Jarmin, and Miranda (2014), “Entrepreneurship and Job Creation in the U.S.,” in process. We also have found the patterns of Figure 4 by examining the share of employment accounted for by young firms. 13 Anecdotal and empirical evidence suggests that high-tech entrepreneurship may have experienced a rebound in the years since our data were collected in March 2011. See for example: PricewaterhouseCoopers (2013), MoneyTree Report, Historical Trend Data; CB Insights (2013), Venture Capital Activity Report; Silicon Valley Bank, Angel Resource Institute, and CB Insights (2013), 2012 Halo Report: Angel Group Activity Year in Review; Silicon Valley Bank, Angel Resource Institute, and CB Insights (2013), Halo Report: Angel Group Update: Q3 2013; Silicon Valley Bank (2012, 2013), Startup Outlook. 14 Daniel E. Hecker, “High-technology employment: a NAICS-based update,” Monthly Labor Review (U.S. Dept. of Labor and U.S. Bureau of Labor Statistics), Volume 128, Number 7, July 2005: 58. 15 For the detailed list, see Table 3 in Hecker, “High-technology employment: a NAICS-based update,” 63. 16 See the Level-I Industries section of Table 1 in Hecker, “High-technology employment: a NAICS-based update,” 60.
Beyond Metropolitan Startup Rates: Regional Factors Associated with Startup Growth This study found that the public sector can affect few significant factors to encourage entrepreneurship except education. Share: Facebook LinkedIn Twitter Download the Report Beyond Metropolitan Startup Rates: Regional Factors Associated with Startup Growth pdf The paper, “Beyond Metropolitan Startup Rates: Regional Factors Associated with Startup Growth,” reports on entrepreneurship activity in 356 U.S. metros as examined from three angles: the startup rate for all industries, the startup rate for high-tech sectors and the rate for high-growth firms. Contrary to the conclusions of most earlier studies, this regionally focused analysis found that the public sector can affect few significant factors to encourage entrepreneurship. For example, despite billions of dollars in government research expenditures, which widely are believed to trickle down to the private sector, area research universities and patents do not contribute to higher rates of entrepreneurship. Education appears to be the most significant factor that the public sector may affect. Metropolitan areas with more college graduates will produce more startups; however, while college completion often is considered the minimum indicator of high skill, the study showed that a higher high school completion rate will further increase the area’s startup rate. Thus, it appears that policymakers best can influence entrepreneurship by finding ways to effectively connect these two factors. Further, the study revealed that: Regions that enjoy substantial venture capital and other financial investments do not necessarily generate a higher ratio of startups. Consequently, a rush to create public venture funds holds little promise of generating more startups or establishing a startup culture.High-tech sectors are hotbeds for high-tech startups, but not for all kinds of startups. Thus, promoting high-tech entrepreneurship does not necessarily elevate the overall economy.Larger metros, not surprisingly, tend to have higher entrepreneurial rates, possibly because their economies are more diverse and resilient than those of smaller cities. This first-ever compilation of metro-level data provides a basis for more rigorous research about regional factors and how they affect startups and entrepreneurial cultures.
Who Started New Businesses in 2013? Share: Facebook LinkedIn Twitter Download the Report Who Started New Business in 2013? pdf Although the United States’ economic upturn remains modest, two indicators from the second annual Kauffman Foundation/LegalZoom survey of entrepreneurs indicated greater confidence in 2013. Small business owners reported lenders providing increased access to credit over the last 12 months, and a greater willingness from the entrepreneurs themselves to put personal savings toward new business ventures. In the survey, titled “Who Started New Businesses in 2013”, and conducted with companies recently formed using LegalZoom, perhaps the strongest evidence of an improving economy is the survey’s operational findings. Thirty-seven percent of new business owners said they had encountered no difficulties in forming or running their companies, a 3 percentage point decrease from 2012. However, of those who did cite difficulties, 36 percent listed unpredictability and 28 percent named lack of access to credit – down a whopping 19 and 17 percentage points, respectively, from 2012. Another encouraging sign was founders’ increased use of personal savings to start their businesses, which may indicate a broader economy-wide shift from savings to investment. Personal savings as a means for funding the startup jumped from 66 percent in 2012 to 86 percent in 2013. Other funding sources included credit cards at 16 percent (10 percent in 2012), retirement savings at 8.5 percent (6.5 percent in 2012) and bank or home equity loans at 6.5 percent (4.5 percent in 2012). Survey findings also showed that businesses with one to four employees increased from 25 percent in 2012 to 26.5 percent in 2013. Twelve percent of the respondents – whose nascent ventures naturally are small – had revenues above $100,000, a 4 percentage point increase over 2012. Further, startups with revenue below $50,000 dropped from 82 percent in 2012 to 77.5 percent in the most recent survey. The higher number of female founders (35 percent, a 4 percentage point increase over 2012) in the most recent survey also hints at a wider economic recovery. More female entrepreneurs may mean that fewer men are leaving salaried work, or that more men are going back to it. Overall, the survey showed nominal shifts in age composition of the sample between 2012 and 2013, with slightly fewer entrepreneurs aged 18-29 and 50-59, and slightly more aged 30-39 and 60+. In 2012, 44.7 percent of respondents had previously started businesses, as compared to 41.4 percent in 2013. Among those who had previously started a business, entrepreneurs who had started more than one other company dropped from 52 percent to 48 percent.
2013 Kauffman LegalZoom Startup Confidence Index Buoyed by belief in an improving economy, the nation’s newest business owners’ confidence leapt in the fourth-quarter 2013 Kauffman/LegalZoom Startup Confidence Index. Share: Facebook LinkedIn Twitter The survey data, released today by the Ewing Marion Kauffman Foundation and LegalZoom, showed that 91 percent of these entrepreneurs were confident or very confident that their companies’ profitability would increase in the next 12 months. The result was a stunning jump of 5 percentage points over the third-quarter survey, and an all-time high for the study, which debuted in first quarter 2012. Leading the way were the youngest entrepreneurs; 18- to 30-year-olds and 31- to 40-year-olds expressed 94 percent and 95 percent confidence levels, respectively. Though confidence dropped 1 percentage point from the previous survey for the 18- to 30-year-olds, it rose 1 point for the 31- to 40-year-olds, marking the highest level for that group in any of the previous surveys. Startup owners’ positive expectations were fostered by growing confidence in U.S. economic performance and consumer demand. Seventy-nine percent are confident the economy will improve or stay the same in the next 12 months, a marked increase of 9 percentage points over the third-quarter survey. Anticipation of growing consumer demand grew higher still, with 55 percent of entrepreneurs saying they believe consumer demand will increase moderately or significantly in the next 12 months, a striking 13 point increase from the previous survey and the highest number in any of the 2013 surveys. Plans to hire additional employees also rose, from 36 percent in the third quarter to 43 percent in the most recent survey. The Kauffman Foundation sponsors the Startup Confidence Index surveys in conjunction with LegalZoom, a leading national provider of online legal solutions and legal plans to young companies. The fourth-quarter findings are based on 1,375 responses to a nationwide, January 2014 survey distributed via email to LegalZoom customers who formed their entities within the last six months. The Index is conducted quarterly to gauge entrepreneurial confidence.
The Most Entrepreneurial Metropolitan Area? Share: Facebook LinkedIn Twitter Download the Report The Most Entrepreneurial Metropolitan Area? pdf This paper, released at the first-ever Mayors Conference during Global Entrepreneurship Week 2013, reports on federal government data – available to the public for the first time – on business startups at the metropolitan area level. By decomposing into four population size classes, the report can provide more effective peer-to-peer comparisons of Metropolitan Statistical Areas (i.e., large vs. large, and small vs. small). The paper compares the trends in forty metropolitan areas with higher numbers of startups over the past two decades to the significant national downward trend in overall new firm formation starting after 2006. Nationally, the trend reversed and started to recover in 2011. No metropolitan area escaped this downward trend, but there are differences among regions in the timing of the downturn and subsequent recovery. The paper demonstrates that Metropolitan Statistical Areas of different locations and sizes can have similar measures of startup density. Surprisingly, many of the MSAs that performed well in the evaluation are not commonly thought of as significant locations of startup activity. The largest MSAs – those with populations greater than 1 million –fared slightly better through the recession and have experienced slightly stronger recoveries, though none has returned to pre-downturn levels.
Analyzing the 2004–2011 KFS Multiply Imputed Data Share: Facebook LinkedIn Twitter Download the Report Analyzing the 2004–2011 KFS Multiply Imputed Data pdf The purpose of this document is to provide instructions to the KFS users regarding the proper use of the KFS multiply imputed data to draw statistically valid inferences in their works. Also, it describes the general framework of the imputation process.
How Cities Can Nurture Cultural Entrepreneurs Share: Facebook LinkedIn Twitter Download the Report How Cities Can Nurture Cultural Entrepreneurs pdf This paper, released at the first-ever Mayors Conference during Global Entrepreneurship Week 2013, discusses the importance of cultural entrepreneurs, particularly since the Great Recession. Author Ann Markusen of the University of Minnesota provides concrete steps that mayors and the public sector can follow to promote cultural entrepreneurship. Economists and city planners increasingly have documented the roles artists play in local economies. As cities have better understood artists’ contributions to the metropolitan economic base – for example, by attracting cultural industry firms and bringing in income from outside the city through exports of books, recordings, visual art, and other creations – their appreciation of and support for artists also have grown. Noting that artists are many times more likely to be self-employed than are scientists and engineers, and that traditional policies and services don’t effectively support artists’ aspirations and occupational training needs, the paper offers seven strategies that mayors and city council members may champion to foster creative entrepreneurs: Know who your local artists are. Encourage convening and equipment-sharing artists’ centers. Develop sustainable artist studios and live/work buildings. Provide entrepreneurial training tailored to artists and designers. Build networking and marketing opportunities for artists. Embed artists in city development strategies. Partner with local arts and policy faculty for entrepreneurial research and training.