How do advanced technology adoption and venture capital (VC) funding impact employment and growth? An analysis of data from the US Census Bureau suggests that while both advanced technology use and VC funding matter on their own for firm outcomes, their joint presence is most strongly correlated with higher employment levels. VC presence is linked with a high increase in employment, though primarily among a limited subset of firms. In contrast, technology adoption is associated with a smaller rise in employment, yet it influences a considerably larger number of firms. A model of startups is created, focusing on decisions to use advanced technology and seek VC funding. The model is compared with firm-level data on employment, advanced technology use, and VC investment. Several thought experiments are conducted using the model. Some experiments assess the importance of advanced technology and VC in the economy. Others examine the reallocation effects across firms with different technology choices and funding sources in response to shifts in taxes and subsidies.
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Dynamics of High-Growth Young Firms and the Role of Venture Capitalists
June 2025
Working Paper Number:
CES-25-38
Motivated by the substantial growth and upfront investments of venture capital (VC) backed firms observed in administrative US Census data, this paper develops a firm dynamics model over the life cycle. In the model, startups choose the source of financing from VC, Angel investors, or banks, depending on their growth potential, and invest in innovation. The calibrated model explains the life-cycle dynamics of firms with different sources of financing and implies that venture capitalists' advice accounts for around 22% of the growth of VC-backed firms. A counterfactual economy without VC financing would lose aggregate consumption by around 0.4%.
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Compositional Nature of Firm Growth and Aggregate Fluctuations
March 2020
Working Paper Number:
CES-20-09
This paper studies firm dynamics over the business cycle. I present evidence from the United Kingdom that more rapidly growing firms are born in expansions than in recessions. Using administrative records from Census data, I find that this observation also holds for the last four recessions in the United States. I also present suggestive evidence that financial frictions play an important role in determining the types of firms that are born at different stages of the business cycle. I then develop a general equilibrium model in which firms choose their managers' span of control at birth. Firms that choose larger spans of control grow faster and eventually get to be larger, and in this sense have a larger target size. Financial frictions in the form of collateral constraints slow the rate at which firms reach their target size. It takes firms longer to get up to scale when collateral constraints tighten; therefore, businesses with the largest target size are affected disproportionately more. Thus, fewer entrepreneurs find it profitable to choose larger projects when financial conditions deteriorate. Using Bayesian methods, I estimate the model using micro and aggregate data from the United Kingdom. I find that financial shocks account for over 80% of fluctuations in the formation of businesses with a large target size, and TFP and labor wedge shocks account for the remaining 20%. An independently estimated version of the model with no choice over the span of control needs larger aggregate shocks in order to account for the same data series, suggesting that the intensive margin of business formation is important at business cycle frequencies. The model with the choice over the span of control generates an empirically relevant and non-targeted collapse in the right tail of the cumulative growth distribution among firms started in recessions, while the model without such a choice does not. The paper also discusses implications for micro-targeted government stimulus policies.
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An Anatomy of U.S. Firms Seeking Trademark Registration
April 2018
Working Paper Number:
CES-18-22
This paper reports on the construction of a new dataset that combines data on trademark applications and registrations from the U.S. Patent and Trademark Office with data on firms from the U.S. Census Bureau. The resulting dataset allows tracking of various activity related to trademark use and protection over the life-cycle of firms, such as the first application for a trademark registration, the first use of a trademark, and the renewal, assignment, and cancellation of trademark registrations. Facts about firm-level trademark activity are documented, including the incidence and timing of trademark registration filings over the firm life-cycle and the connection between firm characteristics and trademark applications. We also explore the relation of trademark application filing to firm employment and revenue growth, and to firm innovative activity as measured by R&D and patents.
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Who Works for Whom? Worker Sorting in a Model of Entrepreneurship with Heterogeneous Labor Markets
January 2015
Working Paper Number:
CES-15-08R
Young and small firms are typically matched with younger and nonemployed individuals, and they provide these workers with lower earnings compared to other firms. To explore the mechanisms behind these facts, a dynamic model of entrepreneurship is introduced, where individuals can choose not to work, become entrepreneurs, or work in one of the two sectors: corporate or entrepreneurial. The differences in production technology, financial constraints, and labor market frictions lead to sector-specific wages and worker sorting across the two sectors. Individuals with lower assets tend to accept lower-paying jobs in the entrepreneurial sector, an implication that finds support in the data. The effect on the entrepreneurial sector of changes in key parameters is also studied to explore some channels that may have contributed to the decline of entrepreneurship in the United States.
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WHO DO UNIONS TARGET? UNIONIZATION OVER THE LIFE-CYCLE OF U.S. BUSINESSES
February 2014
Working Paper Number:
CES-14-09R
What type of businesses do unions target for organizing and when? A dynamic model of the union organizing process is constructed to answer this question. A union monitors establishments in an industry to learn about their productivity, and decides which ones to organize and when. An establishment becomes unionized if the union targets it for organizing and wins the union certification election. The model predicts two main selection effects: unions target larger and more productive establishments early in their life-cycles, and among the establishments targeted, unions are more likely to win elections in smaller and less productive ones. These predictions find support in union certification elections data for 1977-2007 matched with data on establishment characteristics.
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Business Formation: A Tale of Two Recessions
January 2021
Working Paper Number:
CES-21-01
The trajectory of new business applications and transitions to employer businesses differ markedly during the Great Recession and COVID-19 Recession. Both applications and transitions to employer startups decreased slowly but persistently in the post-Lehman crisis period of the Great Recession. In contrast, during the COVID-19 Recession new applications initially declined but have since sharply rebounded, resulting in a surge in applications during 2020. Projected transitions to employer businesses also rise but this is dampened by a change in the composition of applications in 2020 towards applications that are more likely to be nonemployers.
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Tracking Firm Use of AI in Real Time: A Snapshot from the Business Trends and Outlook Survey
March 2024
Working Paper Number:
CES-24-16R
Timely and accurate measurement of AI use by firms is both challenging and crucial for understanding the impacts of AI on the U.S. economy. We provide new, real-time estimates of current and expected future use of AI for business purposes based on the Business Trends and Outlook Survey for September 2023 to February 2024. During this period, bi-weekly estimates of AI use rate rose from 3.7% to 5.4%, with an expected rate of about 6.6% by early Fall 2024. The fraction of workers at businesses that use AI is higher, especially for large businesses and in the Information sector. AI use is higher in large firms but the relationship between AI use and firm size is non-monotonic. In contrast, AI use is higher in young firms. Common uses of AI include marketing automation, virtual agents, and data/text analytics. AI users often utilize AI to substitute for worker tasks and equipment/software, but few report reductions in employment due to AI use. Many firms undergo organizational changes to accommodate AI, particularly by training staff, developing new workflows, and purchasing cloud services/storage. AI users also exhibit better overall performance and higher incidence of employment expansion compared to other businesses. The most common reason for non-adoption is the inapplicability of AI to the business.
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AI 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.
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Wage Dynamics along the Life-Cycle of Manufacturing Plants
August 2011
Working Paper Number:
CES-11-24R
This paper explores the evolution of average wage paid to employees along the life-cycle of a manufacturing plant in U.S. Average wage starts out low for a new plant and increases along with labor productivity, as the plant survives and ages. As a plant experiences productivity decline and approaches exit, average wage falls, but more slowly than it rises in the case of surviving new plants. Moreover, average wage declines slower than productivity does in failing plants, while it rises relatively faster as productivity increases in surviving new plants. These empirical regularities are studied in a dynamic model of labor quality and quantity choice by plants, where labor quality is reflected in wages. The model's parameters are estimated to assess the costs a plant incurs as it alters its labor quality and quantity in response to changes in its productivity over its life-cycle.
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