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Entrepreneurial 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.
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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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Measuring the Effect of COVID-19 on U.S. Small Businesses: The Small Business Pulse Survey
May 2020
Working Paper Number:
CES-20-16
In response to the novel coronavirus (COVID-19) pandemic, the Census Bureau developed and fielded an entirely new survey intended to measure the effect on small businesses. The Small Business Pulse Survey (SBPS) will run weekly from April 26 to June 27, 2020. Results from the SBPS will be published weekly through a visualization tool with downloadable data. We describe the motivation for SBPS, summarize how the content for the survey was developed, and discuss some of the initial results from the survey. We also describe future plans for the SBPS collections and for our research using the SBPS data. Estimates from the first week of the SBPS indicate large to moderate negative effects of COVID-19 on small businesses, and yet the majority expect to return to usual level of operations within the next six months. Reflecting the Census Bureau's commitment to scientific inquiry and transparency, the micro data from the SBPS will be available to qualified researchers on approved projects in the Federal Statistical Research Data Center network.
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Between Firm Changes in Earnings Inequality: The Dominant Role of Industry Effects
February 2020
Working Paper Number:
CES-20-08
We find that most of the rising between firm earnings inequality that dominates the overall increase in inequality in the U.S. is accounted for by industry effects. These industry effects stem from rising inter-industry earnings differentials and not from changing distribution of employment across industries. We also find the rising inter-industry earnings differentials are almost completely accounted for by occupation effects. These results link together the key findings from separate components of the recent literature: one focuses on firm effects and the other on occupation effects. The link via industry effects challenges conventional wisdom.
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Nonemployer Statistics by Demographics (NES-D):
Exploring Longitudinal Consistency and Sub-national Estimates
December 2019
Working Paper Number:
CES-19-34
Until recently, the quinquennial Survey of Business Owners (SBO) was the only source of information for U.S. employer and nonemployer businesses by owner demographic characteristics such as race, ethnicity, sex and veteran status. Now, however, the Nonemployer Statistics by Demographics series (NES-D) will replace the SBO's nonemployer component with reliable, and more frequent (annual) business demographic estimates with no additional respondent burden, and at lower imputation rates and costs. NES-D is not a survey; rather, it exploits existing administrative and census records to assign demographic characteristics to the universe of approximately 25 million (as of 2016) nonemployer businesses.
Although only in the second year of its research phase, NES-D is rapidly moving towards production, with a planned prototype or experimental version release of 2017 nonemployer data in 2020, followed by annual releases of the series. After the first year of research, we released a working paper (Luque et al., 2019) that assessed the viability of estimating nonemployer demographics exclusively with administrative records (AR) and census data. That paper used one year of data (2015) to produce preliminary tabulations of business counts at the national level. This year we expand that research in multiple ways by: i) examining the longitudinal consistency of administrative and census records coverage, and of our AR-based demographics estimates, ii) evaluating further coverage from additional data sources, iii) exploring estimates at the sub-national level, iv) exploring estimates by industrial sector, v) examining demographics estimates of business receipts as well as of counts, and vi) implementing imputation of missing demographic values.
Our current results are consistent with the main findings in Luque et al. (2019), and show that high coverage and demographic assignment rates are not the exception, but the norm. Specifically, we find that AR coverage rates are high and stable over time for each of the three years we examine, 2014-2016. We are able to identify owners for approximately 99 percent of nonemployer businesses (excluding C-corporations), 92 to 93 percent of identified nonemployer owners have no missing demographics, and only about 1 percent are missing three or more demographic characteristics in each of the three years. We also find that our demographics estimates are stable over time, with expected small annual changes that are consistent with underlying population trends in the U.S.. Due to data limitations, these results do not include C-corporations, which represent only 2 percent of nonemployer businesses and 4 percent of receipts.
Without added respondent burden and at lower imputation rates and costs, NES-D will provide high-quality business demographics estimates at a higher frequency (annual vs. every 5 years) 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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The Effects of Industry Classification Changes on US Employment Composition
June 2018
Working Paper Number:
CES-18-28
This paper documents the extent to which compositional changes in US employment from 1976 to 2009 are due to changes in the industry classification scheme used to categorize economic
activity. In 1997, US statistical agencies began implementation of a change from the Standard Industrial Classification System (SIC) to the North American Industrial Classification System (NAICS). NAICS was designed to provide a consistent classification scheme that consolidated declining or obsolete industries and added categories for new industries. Under NAICS, many activities previously classified as Manufacturing, Wholesale Trade, or Retail Trade were re-classified into the Services sector. This re-classification resulted in a significant shift of measured activities across sectors without any change in underlying economic activity. Using a newly developed establishment-level database of employment activity that is consistently classified on a NAICS basis, this paper shows that the change from SIC to NAICS increased the share of Services employment by approximately 36 percent. 7.6 percent of US manufacturing employment, equal to approximately 1.4 million jobs, was reclassified to services. Retail trade and wholesale trade also experienced a significant reclassification of activities in the transition.
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Reservation Employer Establishments: Data from the U.S. Census Longitudinal Business Database
January 2017
Working Paper Number:
CES-17-57
The presence of employers and jobs on American Indian reservations has been difficult to analyze due to limited data. We are the first to geocode confidential data on employer establishments from the U.S. Census Longitudinal Business Database to identify location on or off American Indian reservations. We identify the per capita establishment count and jobs in reservation-based employer establishments for most federally recognized reservations. Comparisons to nearby non-reservation areas in the lower 48 states across 18 industries reveal that reservations have a similar sectoral distribution of employer establishments but have significantly fewer of them in nearly all sectors, especially when the area population is below 15,000 (as it is on the vast majority of reservations and for the majority of the reservation population). By contrast, the total number of jobs provided by reservation establishments is, on average, at par with or somewhat higher than in nearby county areas but is concentrated among casino-related and government employers. An implication is that average job numbers per establishment are higher in these sectors on reservations, including those with populations below 15,000, while the remaining industries are typically sparser within reservations (in firm count and jobs per capita). Geographic and demographic factors, such as population density and per capita income, statistically account for some but not all of these differences.
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Estimating the Local Productivity Spillovers from Science
January 2017
Working Paper Number:
CES-17-56
We estimate the local productivity spillovers from science by relating wages and real estate
prices across metros to measures of scienti c activity in those metros. We address three fundamental challenges: (1) factor input adjustments using wages and real estate prices, along with Shepards Lemma, to estimate changes metros' productivity, which must equal changes in unit production cost; (2) unobserved differences in metros/causality using a share shift index that exploits historic variation in the mix of research in metros interacted with trends in federal funding for specific fields as an instrument; (3) unobserved differences in workers using data on the states in which people are born. Our estimates show a strong positive relationship between wages and scientifc research and a weak positive relationship for real estate prices. Overall, we estimate high rate of return to research.
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Hires and Separations in Equilibrium
January 2016
Working Paper Number:
CES-16-57
Hiring occurs primarily to fill vacant slots that occur when workers separate. Equivalently, separation occurs to move workers to better alternatives. A model of efficient separations yields several specific predictions. Labor market churn is most likely when mean wages are low and the variance in wages is high. Additionally, over the business cycle, churn decreases during recessions, with hires falling at the beginning of recessions and separations declining later to match hiring. Furthermore, the young disproportionately bear the brunt of employment declines. More generally, hires and separations are positively correlated over time as well as across industry and firm. These predictions are borne out in the LEHD microdata at the economy and firm level.
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