Age and High-Growth Entrepreneurship
April 2018
Working Paper Number:
carra-2018-03
Abstract
Many observers, and many investors, believe that young people are especially likely to produce the most successful new firms. We use administrative data at the U.S. Census Bureau to study the ages of founders of growth-oriented start-ups in the past decade. Our primary finding is that successful entrepreneurs are middle-aged, not young. The mean founder age for the 1 in 1,000 fastest growing new ventures is 45.0. The findings are broadly similar when considering high-technology sectors, entrepreneurial hubs, and successful firm exits. Prior experience in the specific industry predicts much greater rates of entrepreneurial success. These findings strongly reject common hypotheses that emphasize youth as a key trait of successful entrepreneurs.
Document Tags and Keywords
Keywords
Keywords are automatically generated using KeyBERT, a powerful and innovative
keyword extraction tool that utilizes BERT embeddings to ensure high-quality and contextually relevant
keywords.
By analyzing the content of working papers, KeyBERT identifies terms and phrases that capture the essence of the
text, highlighting the most significant topics and trends. This approach not only enhances searchability but
provides connections that go beyond potentially domain-specific author-defined keywords.
:
researcher,
growth,
invention,
corporation,
entrepreneurial,
startup,
venture,
entrepreneur,
entrepreneurship,
investor,
proprietor,
innovation,
inventory,
wealth,
funding,
founder,
earner
Tags
Tags are automatically generated using a pretrained language model from spaCy, which excels at
several tasks, including entity tagging.
The model is able to label words and phrases by part-of-speech,
including "organizations." By filtering for frequent words and phrases labeled as "organizations", papers are
identified to contain references to specific institutions, datasets, and other organizations.
:
Internal Revenue Service,
Bureau of Labor Statistics,
New York Times,
Census Bureau Longitudinal Business Database,
Longitudinal Business Database,
Initial Public Offering,
Employer Identification Numbers,
North American Industry Classification System,
Patent and Trademark Office,
Social Security Number,
Longitudinal Employer Household Dynamics,
Business Register,
Protected Identification Key,
W-2,
Census Numident,
Personally Identifiable Information,
Annual Survey of Entrepreneurs
Similar Working Papers
Similarity between working papers are determined by an unsupervised neural
network model
know as Doc2Vec.
Doc2Vec is a model that represents entire documents as fixed-length vectors, allowing for the
capture of semantic meaning in a way that relates to the context of words within the document. The model learns to
associate a unique vector with each document while simultaneously learning word vectors, enabling tasks such as
document classification, clustering, and similarity detection by preserving the order and structure of words. The
document vectors are compared using cosine similarity/distance to determine the most similar working papers.
Papers identified with đŸ”¥ are in the top 20% of similarity.
The 10 most similar working papers to the working paper 'Age and High-Growth Entrepreneurship' are listed below in order of similarity.
-
Working PaperOn the Lifecycle Dynamics of Venture-Capital- and Non-Venture-Capital-Financed Firms
May 2008
Working Paper Number:
CES-08-13
We use a new data set that tracks U.S. firms from their birth over two decades to understand the life cycle dynamics and outcomes (both successes and failures) of VC- and non-VC financed firms. We first ask to what market-wide and firm-level characteristics venture capitalists respond in choosing to make their investments and how this differs for firms financed solely by non-VC sources of entrepreneurial capital. We then ask what are the eventual differences in outcomes for firms that receive VC financing relative to non-VC-financed firms. Our findings suggest that VCs follow public market signals similar to other investors and typically invest largely in young firms, with potential for large scale being an important criterion. The main way that VC financed firms differ from matched non-VC financed firms, is they demonstrate remarkably larger scale both for successful and failed firms, at every point of the firms' life cycle. They grow more rapidly, but we see little difference in profitability measures at times of exit. We further examine a number of hypotheses relating to VC-financed firms' failure. We find that VC-financed firms' cumulative failure rates are lower than non-VC-financed firms but the story is nuanced. VC appears initially 'patient' in that VC-financed firms are less likely to fail in the first five years but conditional on surviving past this point become more likely to fail relative to non-VC-financed firms. We perform a number of robustness checks and find that VC does not appear to have more stringent survival thresholds nor do VC-financed firm failures appear to be disguised as acquisitions nor do particular kinds of VC firms seem to be driving our results. Overall, our analysis supports the view that VC is 'patient' capital relative to other non-VC sources of entrepreneurial capital in the early part of firms' lifecycles and that an important criterion for receiving VC investment is potential for large scale, rather than level of profitability, prior to exit.View Full Paper PDF
-
Working PaperAI Adoption in America: Who, What, and Where
September 2023
Working Paper Number:
CES-23-48R
We study the early adoption and diffusion of five AI-related technologies (automated-guided vehicles, machine learning, machine vision, natural language processing, and voice recognition) as documented in the 2018 Annual Business Survey of 850,000 firms across the United States. We find that fewer than 6% of firms used any of the AI-related technologies we measure, though most very large firms reported at least some AI use. Weighted by employment, average adoption was just over 18%. AI use in production, while varying considerably by industry, nevertheless was found in every sector of the economy and clustered with emerging technologies such as cloud computing and robotics. Among dynamic young firms, AI use was highest alongside more educated, more-experienced, and younger owners, including owners motivated by bringing new ideas to market or helping the community. AI adoption was also more common alongside indicators of high-growth entrepreneurship, including venture capital funding, recent product and process innovation, and growth-oriented business strategies. Early adoption was far from evenly distributed: a handful of 'superstar' cities and emerging hubs led startups' adoption of AI. These patterns of early AI use foreshadow economic and social impacts far beyond this limited initial diffusion, with the possibility of a growing 'AI divide' if early patterns persist.View Full Paper PDF
-
Working PaperWhere Has All the Skewness Gone? The Decline in High-Growth (Young) Firms in the U.S.
November 2015
Working Paper Number:
CES-15-43
The pace of business dynamism and entrepreneurship in the U.S. has declined over recent decades. We show that the character of that decline changed around 2000. Since 2000 the decline in dynamism and entrepreneurship has been accompanied by a decline in high-growth young firms. Prior research has shown that the sustained contribution of business startups to job creation stems from a relatively small fraction of high-growth young firms. The presence of these high-growth young firms contributes to a highly (positively) skewed firm growth rate distribution. In 1999, a firm at the 90th percentile of the employment growth rate distribution grew about 31 percent faster than the median firm. Moreover, the 90-50 differential was 16 percent larger than the 50-10 differential reflecting the positive skewness of the employment growth rate distribution. We show that the shape of the firm employment growth distribution changes substantially in the post-2000 period. By 2007, the 90-50 differential was only 4 percent larger than the 50-10, and it continued to exhibit a trend decline through 2011. The reflects a sharp drop in the 90th percentile of the growth rate distribution accounted for by the declining share of young firms and the declining propensity for young firms to be high-growth firms.View Full Paper PDF
-
Working PaperHiring through Startup Acquisitions: Preference Mismatch and Employee Departures
September 2018
Working Paper Number:
CES-18-41
This paper investigates the effectiveness of startup acquisitions as a hiring strategy. Unlike conventional hires who choose to join a new firm on their own volition, most acquired employees do not have a voice in the decision to be acquired, much less by whom to be acquired. The lack of worker agency may result in a preference mismatch between the acquired employees and the acquiring firm, leading to elevated rates of turnover. Using comprehensive employee-employer matched data from the US Census, I document that acquired workers are significantly more likely to leave compared to regular hires. By constructing a novel peer-based proxy for worker preferences, I show that acquired employees who prefer to work for startups ' rather than established firms ' are the most likely to leave after the acquisition, lending support to the preference mismatch theory. Moreover, these departures suggest a deeper strategic cost of competitive spawning: upon leaving, acquired workers are more likely to found their own companies, many of which appear to be competitive threats that impair the acquirer's long-run performance.View Full Paper PDF
-
Working PaperHigh-Growth Entrepreneurship
January 2017
Working Paper Number:
CES-17-53
We study the patterns and determinants of job creation for a large cohort of start-up firms. Analysis of the universe of U.S. employers reveals strong persistence in employment size from firm birth to age seven, with a small fraction of firms accounting for most employment at both ages, patterns that are little explained by finely disaggregated industry controls or amount of finance. Linking to data from the Survey of Business Owners on characteristics of 54,700 founders of 36,400 start-ups, and defining 'high growth' as the top 5% of firms in the size distribution at age zero and seven, we find that women have a 30% lower probability of founding high-growth entrepreneurships at both ages. A similar gap for African-Americans at start-up disappears by age seven. Other differences with respect to race, ethnicity, and nativity are modest. Founder age is initially positively associated with high growth probability but the profile flattens after seven years and even becomes slightly negative. The education profile is initially concave, with advanced degree recipients no more likely to found high growth firms than high school graduates, but the former catch up to those with bachelor's degrees by firm age seven, while the latter do not. Most other relationships of high growth with founder characteristics are highly persistent over time. Prior business ownership is strongly positively associated, and veteran experience negatively associated, with high growth. A larger founding team raises the probability of high growth, while diversity (by gender, age, race/ethnicity, or nativity) either lowers the probability or has little effect. More start-up capital raises the high-growth propensity of firms founded by a sole proprietor, women, minorities, immigrants, veterans, novice entrepreneurs, and those who are younger or with less education. Perhaps surprisingly, women, minorities, and those with less education tend to choose high growth industries, but fewer of them achieve high growth compared to their industry peers.View Full Paper PDF
-
Working PaperThe Promise and Potential of Linked Employer-Employee Data for Entrepreneurship Research
September 2015
Working Paper Number:
CES-15-29
In this paper, we highlight the potential for linked employer-employee data to be used in entrepreneurship research, describing new data on business start-ups, their founders and early employees, and providing examples of how they can be used in entrepreneurship research. Linked employer-employee data provides a unique perspective on new business creation by combining information on the business, workforce, and individual. By combining data on both workers and firms, linked data can investigate many questions that owner-level or firm-level data cannot easily answer alone - such as composition of the workforce at start-ups and their role in explaining business dynamics, the flow of workers across new and established firms, and the employment paths of the business owners themselves.View Full Paper PDF
-
Working PaperWho Works for Startups? The Relation between Firm Age, Employee Age, and Growth
October 2011
Working Paper Number:
CES-11-31
We present evidence that young employees are an important ingredient in the creation and growth of firms. Our results suggest that young employees possess attributes or skills, such as willingness to take risk or innovativeness, which make them relatively more valuable in young, high growth, firms. Young firms disproportionately hire young employees, controlling for firm size, industry, geography and time. Young employees in young firms command higher wages than young employees in older firms and earn wages that are relatively more equal to older employees within the same firm. Moreover, young employees disproportionately join young firms that subsequently exhibit higher growth and raise venture capital financing. Finally, we show that an increase in the regional supply of young workers increases the rate of new firm creation. Our results are relevant for investors and executives in young, high growth, firms, as well as policymakers interested in fostering entrepreneurship.View Full Paper PDF
-
Working PaperFinancing, Ownership, and Performance: A Novel, Longitudinal Firm-Level Database
December 2024
Working Paper Number:
CES-24-73
The Census Bureau's Longitudinal Business Database (LBD) underpins many studies of firm-level behavior. It tracks longitudinally all employers in the nonfarm private sector but lacks information about business financing and owner characteristics. We address this shortcoming by linking LBD observations to firm-level data drawn from several large Census Bureau surveys. The resulting Longitudinal Employer, Owner, and Financing (LEOF) database contains more than 3 million observations at the firm-year level with information about start-up financing, current financing, owner demographics, ownership structure, profitability, and owner aspirations ' all linked to annual firm-level employment data since the firm hired its first employee. Using the LEOF database, we document trends in owner demographics and financing patterns and investigate how these business characteristics relate to firm-level employment outcomes.View Full Paper PDF
-
Working PaperEntrepreneurial Teams: Diversity of Skills and Early-Stage Growth
December 2020
Working Paper Number:
CES-20-45
We use employer-employee linked data to track the employment histories of team members prior to startup formation for a full cohort of new firms in the U.S. Using pre-startup industry experience to measure skillsets, we find that startups that have founding teams with more diverse collective skillsets grow faster than peer firms in the same industries and local economies. A one standard deviation increase in teams' skill diversity is associated with an increase in five-year employment (sales) growth of 16% (10%) from the mean. The effects are stronger among startups in innovative industries and among startups facing greater ex-ante uncertainty. Moreover, the results are robust to a variety of approaches to address the endogeneity of team composition. Overall, our results suggest that teams with more diverse collective skillsets adapt their strategies more successfully in the uncertain environments faced by (innovative) startup firms.View Full Paper PDF
-
Working PaperWho Creates Jobs? Small vs. Large vs. Young
August 2010
Working Paper Number:
CES-10-17
There's been a long, sometimes heated, debate on the role of firm size in employment growth. Despite skepticism in the academic community, the notion that growth is negatively related to firm size remains appealing to policymakers and small business advocates. The widespread and repeated claim from this community is that most new jobs are created by small businesses. Using data from the Census Bureau Business Dynamics Statistics and Longitudinal Business Database, we explore the many issues regarding the role of firm size and growth that have been at the core of this ongoing debate (such as the role of regression to the mean). We find that the relationship between firm size and employment growth is sensitive to these issues. However, our main finding is that once we control for firm age there is no systematic relationship between firm size and growth. Our findings highlight the important role of business startups and young businesses in U.S. job creation. Business startups contribute substantially to both gross and net job creation. In addition, we find an 'up or out' dynamic of young firms. These findings imply that it is critical to control for and understand the role of firm age in explaining U.S. job creation.View Full Paper PDF
