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Startup Dynamics: Transitioning from Nonemployer Firms to Employer Firms, Survival, and Job Creation
April 2025
Working Paper Number:
CES-25-26
Understanding the dynamics of startup businesses' growth, exit, and survival is crucial for fostering entrepreneurship. Among the nearly 30 million registered businesses in the United States, fewer than six million have employees beyond the business owners. This research addresses the gap in understanding which companies transition to employer businesses and the mechanisms behind this process. Job creation remains a critical concern for policymakers, researchers, and advocacy groups. This study aims to illuminate the transition from non-employer businesses to employer businesses and explore job creation by new startups. Leveraging newly available microdata from the U.S. Census Bureau, we seek to gain deeper insights into firm survival, job creation by startups, and the transition from non-employer to employer status.
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Growth is Getting Harder to Find, Not Ideas
April 2025
Working Paper Number:
CES-25-21
Relatively flat US output growth versus rising numbers of US researchers is often interpreted as evidence that "ideas are getting harder to find." We build a new 46-year panel tracking the universe of U.S. firms' patenting to investigate the micro underpinnings of this claim, separately examining the relationships between research inputs and ideas (patents) versus ideas and growth. Over our sample period, we find that researchers' patenting productivity is increasing, there is little evidence of any secular decline in high-quality patenting common to all firms, and the link between patents and growth is present, differs by type of idea, and is fairly stable. On the other hand, we find strong evidence of secular decreases in output unrelated to patenting, suggesting an important role for other factors. Together, these results invite renewed empirical and theoretical attention to the impact of ideas on growth. To that end, our patent-firm bridge, which will be available to researchers with approved access, is used to produce new, public-use statistics on the Business Dynamics of Patenting Firms (BDS-PF).
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Urban-Biased Growth: A Macroeconomic Analysis
June 2024
Working Paper Number:
CES-24-33
After 1980, larger US cities experienced substantially faster wage growth than smaller ones. We show that this urban bias mainly reflected wage growth at large Business Services firms. These firms stand out through their high per-worker expenditure on information technology and disproportionate presence in big cities. We introduce a spatial model of investment-specific technical change that can rationalize these patterns. Using the model as an accounting framework, we find that the observed decline in the investment price of information technology capital explains most urban-biased growth by raising the profits of large Business Services firms in big cities.
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The Changing Firm and Country Boundaries of US Manufacturers in Global Value Chains
July 2023
Working Paper Number:
CES-23-38
This paper documents how US firms organize goods production across firm and country boundaries. Most US firms that perform physical transformation tasks in-house using foreign manufacturing plants in 2007 also own US manufacturing plants; moreover manufacturing comprises their main domestic activity. By contrast, 'factoryless goods producers' outsource all physical transformation tasks to arm's-length contractors, focusing their in-house efforts on design and marketing. This distinct firm type is missing from standard analyses of manufacturing, growing in importance, and increasingly reliant on foreign suppliers. Physical transformation 'within-the-firm' thus coincides with substantial physical transformation 'within-the-country,' whereas its performance 'outside-the-firm' often also implies 'outside-the-country.' Despite these differences, factoryless goods producers and firms with foreign and domestic manufacturing plants both employ relatively high shares of US knowledge workers. These patterns call for new models and data to capture the potential for foreign production to support domestic innovation, which US firms leverage around the world.
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Trade Liberalization and Labor-Market Outcomes: Evidence from US Matched Employer-Employee Data
September 2022
Working Paper Number:
CES-22-42
We use matched employer-employee data to examine outcomes among workers initially employed within and outside manufacturing after trade liberalization with China. We find that exposure to this shock operates predominantly through workers' counties (versus industries), that larger own industry and downstream exposure typically reduce relative earnings, and that greater upstream exposure often raises them. The latter is particularly important outside manufacturing: while we find substantial and persistent predicted declines in relative earnings among manufacturing workers, those outside manufacturing are generally predicted to experience relative earnings gains. Investigation of employment reactions indicates they account for a small share of the earnings effect.
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Multinational Firms in the U.S. Economy: Insights from Newly Integrated Microdata
September 2022
Working Paper Number:
CES-22-39
This paper describes the construction of two confidential crosswalk files enabling a comprehensive identification of multinational rms in the U.S. economy. The effort combines firm-level surveys on direct investment conducted by the U.S. Bureau of Economic Analysis (BEA) and the U.S. Census Bureau's Business Register (BR) spanning the universe of employer businesses from 1997 to 2017. First, the parent crosswalk links BEA firm-level surveys on U.S. direct investment abroad and the BR. Second, the affiliate crosswalk links BEA firm-level surveys on foreign direct investment in the United States and the BR. Using these newly available links, we distinguish between U.S.- and foreign-owned multinational firms and describe their prevalence and economic activities in the national economy, by sector, and by geography.
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Advanced Technologies Adoption and Use by U.S. Firms: Evidence from the Annual Business Survey
December 2020
Working Paper Number:
CES-20-40
We introduce a new survey module intended to complement and expand research on the causes and consequences of advanced technology adoption. The 2018 Annual Business Survey (ABS), conducted by the Census Bureau in partnership with the National Center for Science and Engineering Statistics (NCSES), provides comprehensive and timely information on the diffusion among U.S. firms of advanced technologies including artificial intelligence (AI), cloud computing, robotics, and the digitization of business information. The 2018 ABS is a large, nationally representative sample of over 850,000 firms covering all private, nonfarm sectors of the economy. We describe the motivation for and development of the technology module in the ABS, as well as provide a first look at technology adoption and use patterns across firms and sectors. We find that digitization is quite widespread, as is some use of cloud computing. In contrast, advanced technology adoption is rare and generally skewed towards larger and older firms. Adoption patterns are consistent with a hierarchy of increasing technological sophistication, in which most firms that adopt AI or other advanced business technologies also use the other, more widely diffused technologies. Finally, while few firms are at the technology frontier, they tend to be large so technology exposure of the average worker is significantly higher. This new data will be available to qualified researchers on approved projects in the Federal Statistical Research Data Center network.
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Business Dynamic Statistics of Innovative Firms
January 2017
Working Paper Number:
CES-17-72
A key driver of economic growth is the reallocation of resources from low to high productivity activities. Innovation plays an important role in this regard by introducing new products, services, and business methods that ultimately lead to increased productivity and rising living standards. Traditional measures of innovation, particularly those based on aggregate inputs, are increasingly unable to capture the breadth and depth of innovation in modern economies. In this paper, we describe an effort at the
US Census Bureau, the Business Dynamics Statistics of Innovative Firms (BDS-IF) project, which aims to address these challenges by extending the Business Dynamics Statistics data to include new measures of innovative activity. The BDS-IF project will produce measures of firm, establishment, and employment flows by firm age, firm size, and industry for the subset of firms engaged in activities related to innovation. These activities include patenting and trademarking, the employment of STEM workers, and R&D expenditures. The exibility of the underlying data infrastructure allows this measurement agenda to be extended to include copyright activity, management practices, and high growth firms.
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The United States Labor Market: Status Quo or A New Normal?
September 2012
Working Paper Number:
CES-12-28
The recession of 2007-09 witnessed high rates of unemployment that have been slow to recede. This has led many to conclude that structural changes have occurred in the labor market and that the economy will not return to the low rates of unemployment that prevailed in the recent past. Is this true? The question is important because central banks may be able to reduce unemployment that is cyclic in nature, but not that which is structural. An analysis of labor market data suggests that there are no structural changes that can explain movements in unemployment rates over recent years. Neither industrial nor demographic shifts nor a mismatch of skills with job vacancies is behind the increased rates of unemployment. Although mismatch increased during the recession, it retreated at the same rate. The patterns observed are consistent with unemployment being caused by cyclic phenomena that are more pronounced during the current recession than in prior recessions.
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Estimating Measurement Error in SIPP Annual Job Earnings: A Comparison of Census Bureau Survey and SSA Administrative Data
July 2011
Working Paper Number:
CES-11-20
We quantify sources of variation in annual job earnings data collected by the Survey of Income and Program Participation (SIPP) to determine how much of the variation is the result of measurement error. Jobs reported in the SIPP are linked to jobs reported in an administrative database, the Detailed Earnings Records (DER) drawn from the Social Security Administration's Master Earnings File, a universe file of all earnings reported on W-2 tax forms. As a result of the match, each job potentially has two earnings observations per year: survey and administrative. Unlike previous validation studies, both of these earnings measures are viewed as noisy measures of some underlying true amount of annual earnings. While the existence of survey error resulting from respondent mistakes or misinterpretation is widely accepted, the idea that administrative data are also error-prone is new. Possible sources of employer reporting error, employee under-reporting of compensation such as tips, and general differences between how earnings may be reported on tax forms and in surveys, necessitates the discarding of the assumption that administrative data are a true measure of the quantity that the survey was designed to collect. In addition, errors in matching SIPP and DER jobs, a necessary task in any use of administrative data, also contribute to measurement error in both earnings variables. We begin by comparing SIPP and DER earnings for different demographic and education groups of SIPP respondents. We also calculate different measures of changes in earnings for individuals switching jobs. We estimate a standard earnings equation model using SIPP and DER earnings and compare the resulting coefficients. Finally exploiting the presence of individuals with multiple jobs and shared employers over time, we estimate an econometric model that includes random person and firm effects, a common error component shared by SIPP and DER earnings, and two independent error components that represent the variation unique to each earnings measure. We compare the variance components from this model and consider how the DER and SIPP differ across unobservable components.
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