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Nonemployer Statistics by Demographics (NES-D): Using Administrative and Census Records Data in Business Statistics
January 2019
Working Paper Number:
CES-19-01
The quinquennial Survey of Business Owners or SBO provided the only comprehensive source of information in the United States on employer and nonemployer businesses by the sex, race, ethnicity and veteran status of the business owners. The annual Nonemployer Statistics series (NES) provides establishment counts and receipts for nonemployers but contains no demographic information on the business owners. With the transition of the employer component of the SBO to the Annual Business Survey, the Nonemployer Statistics by Demographics series or NES-D represents the continuation of demographics estimates for nonemployer businesses. NES-D will leverage existing administrative and census records to assign demographic characteristics to the universe of approximately 24 million nonemployer businesses (as of 2015). Demographic characteristics include key demographics measured by the SBO (sex, race, Hispanic origin and veteran status) as well as other demographics (age, place of birth and citizenship status) collected but not imputed by the SBO if missing. A spectrum of administrative and census data sources will provide the nonemployer universe and demographics information. Specifically, the nonemployer universe originates in the Business Register; the Census Numident will provide sex, age, place of birth and citizenship status; race and Hispanic origin information will be obtained from multiple years of the decennial census and the American Community Survey; and the Department of Veteran Affairs will provide administrative records data on veteran status.
The use of blended data in this manner will make possible the production of NES-D, an annual series that will become the only source of detailed and comprehensive statistics on the scope, nature and activities of U.S. businesses with no paid employment by the demographic characteristics of the business owner. Using the 2015 vintage of nonemployers, initial results indicate that demographic information is available for the overwhelming majority of the universe of nonemployers. For instance, information on sex, age, place of birth and citizenship status is available for over 95 percent of the 24 million nonemployers while race and Hispanic origin are available for about 90 percent of them. These results exclude owners of C-corporations, which represent only 2 percent of nonemployer firms. Among other things, future work will entail imputation of missing demographics information (including that of C-corporations), testing the longitudinal consistency of the estimates, and expanding the set of characteristics beyond the demographics mentioned above. Without added respondent burden and at lower imputation rates and costs, NES-D will meet the needs of stakeholders as well as the economy as a whole by providing reliable estimates at a higher frequency (annual vs. every 5 years) and with a more timely dissemination schedule than the SBO.
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Reservation Nonemployer and Employer Establishments: Data from U.S. Census Longitudinal Business Databases
December 2018
Working Paper Number:
CES-18-50
The presence of businesses on American Indian reservations has been difficult to analyze due to limited data. Akee, Mykerezi, and Todd (AMT; 2017) geocoded confidential data from the U.S. Census Longitudinal Business Database to identify whether employer establishments were located on or off American Indian reservations and then compared federally recognized reservations and nearby county areas with respect to their per capita number of employers and jobs. We use their methods and the U.S. Census Integrated Longitudinal Business Database to develop parallel results for nonemployer establishments and for the combination of employer and nonemployer establishments. Similar to AMT's findings, we find that reservations and nearby county areas have a similar sectoral distribution of nonemployer and nonemployer-plus-employer establishments, but reservations have significantly fewer of them in nearly all sectors, especially when the area population is below 15,000. By contrast to AMT, the average size of reservation nonemployer establishments, as measured by revenue (instead of the jobs measure AMT used for employers), is smaller than the size of nonemployers in nearby county areas, and this is true in most industries as well. The most significant exception is in the retail sector. Geographic and demographic factors, such as population density and per capita income, statistically account for only a small portion of these differences. However, when we assume that nonemployer establishments create the equivalent of one job and use combined employer-plus-nonemployer jobs to measure establishment size, the employer job numbers dominate and we parallel AMT's finding that, due to large job counts in the Arts/Entertainment/Recreation and Public Administration sectors, reservations on average have slightly more jobs per resident than nearby county areas.
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Occupational Classifications: A Machine Learning Approach
August 2018
Working Paper Number:
CES-18-37
Characterizing the work that people do on their jobs is a longstanding and core issue in labor economics. Traditionally, classification has been done manually. If it were possible to combine new computational tools and administrative wage records to generate an automated crosswalk between job titles and occupations, millions of dollars could be saved in labor costs, data processing could be sped up, data could become more consistent, and it might be possible to generate, without a lag, current information about the changing occupational composition of the labor market. This paper examines the potential to assign occupations to job titles contained in administrative data using automated, machine-learning approaches. We use a new extraordinarily rich and detailed set of data on transactional HR records of large firms (universities) in a relatively narrowly defined industry (public institutions of higher education) to identify the potential for machine-learning approaches to classify occupations.
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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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The Employee Clientele of Corporate Leverage: Evidence from Personal Labor Income Diversification
January 2018
Working Paper Number:
CES-18-01
Using employee job-level data, we empirically test the equilibrium matching between a firm's debt usage and its employee job risk aversion ('clientele effect'), as predicted by the existing theories. We measure job risk aversion for a firm's employees using their labor income concentration in the firm, calculated as the fraction of the employees' total personal labor income or total household labor
income that is accounted for by their income from this particular firm. Using a sample of about 1,400 U.S. public firms from 1990-2008, we find a robust negative relation between leverage and employee job risk aversion, which is consistent with the clientele effect. Specifically, when a firm's existing employees have higher labor income concentration in it, the firm tends to have lower contemporaneous and future leverage. Moreover, in terms of new hires, firms with lower leverage are more likely to recruit employees with less alternative labor income. Our results continue to hold after we control for firm fixed effects, other employee characteristics such as wages, gender, age, race, and education, and managerial risk attitudes. Further, the matching between a firm's leverage and its workers' labor income concentration in it is more pronounced for firms with higher labor intensity and those in financial distress.
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The Effects of Occupational Licensing Evidence from Detailed Business-Level Data
January 2017
Working Paper Number:
CES-17-20
Occupational licensing regulation has increased dramatically in importance over the last several decades, currently affecting more than one thousand occupations in the United States. I use confidential U.S. Census Bureau micro-data to study the relationship between occupational licensing and key business outcomes, such as number of practitioners, prices for consumers, and practitioners' entry and exit rates. The paper sheds light on the effect of occupational licensing on industry dynamics and intensity of competition, and is the first to study the effects on providers of required occupational training. I find that occupational licensing regulation does not affect the equilibrium number of practitioners or prices of services to consumers, but reduces significantly practitioner entry and exit rates. I further find that providers of occupational licensing training, namely, schools, are larger and seem to do better, in terms of revenues and gross margins, in states with more stringent occupational licensing regulation.
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Taking the Leap: The Determinants of Entrepreneurs Hiring their First Employee
January 2016
Working Paper Number:
CES-16-48
Job creation is one of the most important aspects of entrepreneurship, but we know relatively little about the hiring patterns and decisions of startups. Longitudinal data from the Integrated Longitudinal Business Database (iLBD), Kauffman Firm Survey (KFS), and the Growing America through Entrepreneurship (GATE) experiment are used to provide some of the first evidence in the literature on the determinants of taking the leap from a non-employer to employer firm among startups. Several interesting patterns emerge regarding the dynamics of non-employer startups hiring their first employee. Hiring rates among the universe of non-employer startups are very low, but increase when the population of non-employers is focused on more growth-oriented businesses such as incorporated and EIN businesses. If non-employer startups hire, the bulk of hiring occurs in the first few years of existence. After this point in time relatively few non-employer startups hire an employee. Focusing on more growth- and employment-oriented startups in the KFS, we find that Asian-owned and Hispanic-owned startups have higher rates of hiring their first employee than white-owned startups. Female-owned startups are roughly 10 percentage points less likely to hire their first employee by the first, second and seventh years after startup. The education level of the owner, however, is not found to be associated with the probability of hiring an employee. Among business characteristics, we find evidence that business assets and intellectual property are associated with hiring the first employee. Using data from the largest random experiment providing entrepreneurship training in the United States ever conducted, we do not find evidence that entrepreneurship training increases the likelihood that non-employers hire their first employee.
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THE IMPACT OF LATINO-OWNED BUSINESS ON LOCAL ECONOMIC PERFORMANCE
January 2016
Working Paper Number:
CES-16-34
This paper takes advantage of the Michigan Census Research Data Center to merge limited-access Census Bureau data with county level information to investigate the impact of Latino-owned business (LOB) employment share on local economic performance measures, namely per capita income, employment, poverty, and population growth. Beginning with OLS and then moving to the Spatial Durbin Model, this paper shows the impact of LOB overall employment share is insignificant. When decomposed into various industries, however, LOB employment share does have a significant impact on economic performance measures. Significance varies by industry, but the results support a divide in the impact of LOB employment share in low and high-barrier industries.
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THE DYNAMICS OF LATINO-OWNED BUSINESS WITH COMPARISIONS TO OTHER ETHNICITIES
January 2016
Working Paper Number:
CES-16-33
This paper employs the Michigan Census Research Data Center to merge three limited-access Census Bureau data sets by individual firm and establishment level to investigate the factors associated with the Latino-owned Business (LOB) location and dynamics over time. The three main LOB outcomes under analysis are as follows: (1) the probability of a business being Latino-owned as opposed to a business being Asian-owned, Black-owned, or White-owned; (2) the probability of new business entry and exit; and (3) LOB employment growth. This paper then compares these factors associated with LOB with past findings on businesses that are Asian-owned, Black-owned, and White-owned. Some notable findings include: (1) only Black business owners are less associated with using personal savings as start-up capital than Latinos; (2) the only significant coefficient on start-up capital source is personal savings and it increases the odds of survival of a Latino business by 4%; (3) on average, having Puerto Rican ancestry decreases the odds of business survival; and (4) LOB are relatively likely to start a business with a small amount of capital, which, in turn, limits their future growth.
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The Annual Survey of Entrepreneurs: An Introduction
November 2015
Working Paper Number:
CES-15-40R
The Census Bureau continually seeks to improve its measures of the U.S. economy as part of its mission. In some cases this means expanding or updating the content of its existing surveys, expanding the use of administrative data, and/or exploring the use of privately collected data. When these options cannot provide the needed data, the Census Bureau may consider fielding a new survey to fill the gap. This paper describes one such new survey, the Annual Survey of Entrepreneurs (ASE). Innovations in content, format, and process are designed to provide high-quality, timely, frequent information on the activities of one of the important drivers of economic growth: entrepreneurship. The ASE is collected through a partnership of the Census Bureau with the Kauffman Foundation and the Minority Business Development Agency. The first wave of the ASE collection started in fall of 2015 (for reference period 2014) and results will be released in summer 2016. Qualified researchers on approved projects will be able to access micro data from the ASE through the Federal Statistical Research Data Center (FSRDC) network.
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