CREAT: Census Research Exploration and Analysis Tool

Who Works for Startups? The Relation between Firm Age, Employee Age, and Growth

October 2011

Working Paper Number:

CES-11-31

Abstract

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.

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.
:
company, growth, employ, venture, entrepreneur, entrepreneurship, investor, financing, younger firms, innovation, innovate, hiring, opportunity, innovative, firms young

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.
:
Standard Statistical Establishment List, Internal Revenue Service, Standard Industrial Classification, National Science Foundation, Ordinary Least Squares, Census Bureau Longitudinal Business Database, Business Services, Longitudinal Business Database, Initial Public Offering, Chicago Census Research Data Center, Employer Identification Numbers, North American Industry Classification System, Alfred P Sloan Foundation, Longitudinal Employer Household Dynamics, Quarterly Workforce Indicators, Business Register Bridge, Harvard Business School, Duke University

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 🔥 are in the top 20% of similarity.

The 10 most similar working papers to the working paper 'Who Works for Startups? The Relation between Firm Age, Employee Age, and Growth' are listed below in order of similarity.