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The Local Origins of Business Formation
July 2023
Working Paper Number:
CES-23-34
What locations generate more business ideas, and where are ideas more likely to turn into businesses? Using comprehensive administrative data on business applications, we analyze the spatial disparity in the creation of business ideas and the formation of new employer startups from these ideas. Startups per capita exhibit enormous variation across granular units of geography. We decompose this variation into variation in ideas per capita and in their rate of transition to startups, and find that both components matter. Observable local demographic, economic, financial, and business conditions accounts for a significant fraction of the variation in startups per capita, and more so for the variation in ideas per capita than in transition rate. Income, education, age, and foreign-born share are generally strong positive correlates of both idea generation and transition. Overall, the relationship of local conditions with ideas differs from that with transition rate in magnitude, and sometimes, in sign: certain conditions (notably, the African-American share of the population) are positively associated with ideas, but negatively with transition rates. We also find a close correspondence between the actual rank of locations in terms of startups per capita and the predicted rank based only on observable local conditions ' a result useful for characterizing locations with high startup activity.
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Quality Adjustment at Scale: Hedonic vs. Exact Demand-Based Price Indices
June 2023
Working Paper Number:
CES-23-26
This paper explores alternative methods for adjusting price indices for quality change at scale. These methods can be applied to large-scale item-level transactions data that in cludes information on prices, quantities, and item attributes. The hedonic methods can take into account the changing valuations of both observable and unobservable charac teristics in the presence of product turnover. The paper also considers demand-based approaches that take into account changing product quality from product turnover and changing appeal of continuing products. The paper provides evidence of substantial quality-adjustment in prices for a wide range of goods, including both high-tech consumer products and food products.
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Self-Employment Income Reporting on Surveys
April 2023
Working Paper Number:
CES-23-19
We examine the relation between administrative income data and survey reports for self-employed and wage-earning respondents from 2000 - 2015. The self-employed report 40 percent more wages and self-employment income in the survey than in tax administrative records; this estimate nets out differences between these two sources that are also shared by wage-earners. We provide evidence that differential reporting incentives are an important explanation of the larger self-employed gap by exploiting a well-known artifact ' self-employed respondents exhibit substantial bunching at the
first EITC kink in their administrative records. We do not observe the same behavior in their survey responses even after accounting for survey measurement concerns.
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Exploring New Ways to Classify Industries for Energy Analysis and Modeling
November 2022
Working Paper Number:
CES-22-49
Combustion, other emitting processes and fossil energy use outside the power sector have become urgent concerns given the United States' commitment to achieving net-zero greenhouse gas emissions by 2050. Industry is an important end user of energy and relies on fossil fuels used directly for process heating and as feedstocks for a diverse range of applications. Fuel and energy use by industry is heterogeneous, meaning even a single product group can vary broadly in its production routes and associated energy use. In the United States, the North American Industry Classification System (NAICS) serves as the standard for statistical data collection and reporting. In turn, data based on NAICS are the foundation of most United States energy modeling. Thus, the effectiveness of NAICS at representing energy use is a limiting condition for current
expansive planning to improve energy efficiency and alternatives to fossil fuels in industry. Facility-level data could be used to build more detail into heterogeneous sectors and thus supplement data from Bureau of the Census and U.S Energy Information Administration reporting at NAICS code levels but are scarce. This work explores alternative classification schemes for industry based on energy use characteristics and validates an approach to estimate facility-level energy use from publicly available greenhouse gas emissions data from the U.S. Environmental Protection Agency (EPA). The approaches in this study can facilitate understanding of current, as well as possible future, energy demand.
First, current approaches to the construction of industrial taxonomies are summarized along with their usefulness for industrial energy modeling. Unsupervised machine learning techniques are then used to detect clusters in data reported from the U.S. Department of Energy's Industrial Assessment Center program. Clusters of Industrial Assessment Center data show similar levels of correlation between energy use and explanatory variables as three-digit NAICS codes. Interestingly, the clusters each include a large cross section of NAICS codes, which lends additional support to the idea that NAICS may not be particularly suited for correlation between energy use and the variables studied. Fewer clusters are needed for the same level of correlation as shown in NAICS codes. Initial assessment shows a reasonable level of separation using support vector machines with higher than 80% accuracy, so machine learning approaches may be promising for further analysis. The IAC data is focused on smaller and medium-sized facilities and is biased toward higher energy users for a given facility type. Cladistics, an approach for classification developed in biology, is adapted to energy and process characteristics of industries. Cladistics applied to industrial systems seeks to understand the progression of organizations and technology as a type of evolution, wherein traits are inherited from previous systems but evolve due to the emergence of inventions and variations and a selection process driven by adaptation to pressures and favorable outcomes. A cladogram is presented for evolutionary directions in the iron and steel sector. Cladograms are a promising tool for constructing scenarios and summarizing directions of sectoral innovation.
The cladogram of iron and steel is based on the drivers of energy use in the sector. Phylogenetic inference is similar to machine learning approaches as it is based on a machine-led search of the solution space, therefore avoiding some of the subjectivity of other classification systems. Our prototype approach for constructing an industry cladogram is based on process characteristics according to the innovation framework derived from Schumpeter to capture evolution in a given sector. The resulting cladogram represents a snapshot in time based on detailed study of process characteristics. This work could be an important tool for the design of scenarios for more detailed modeling. Cladograms reveal groupings of emerging or dominant processes and their implications in a way that may be helpful for policymakers and entrepreneurs, allowing them to see the larger picture, other good ideas, or competitors. Constructing a cladogram could be a good first step to analysis of many industries (e.g. nitrogenous fertilizer production, ethyl alcohol manufacturing), to understand their heterogeneity, emerging trends, and coherent groupings of related innovations.
Finally, validation is performed for facility-level energy estimates from the EPA Greenhouse Gas Reporting Program. Facility-level data availability continues to be a major challenge for industrial modeling. The method outlined by (McMillan et al. 2016; McMillan and Ruth 2019) allows estimating of facility level energy use based on mandatory greenhouse gas reporting. The validation provided here is an important step for further use of this data for industrial energy modeling.
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R&D or R vs. D?
Firm Innovation Strategy and Equity Ownership
April 2020
Working Paper Number:
CES-20-14
We analyze a unique dataset that separately reports research and development expenditures
for a large panel of public and private firms. Definitions of 'research' and 'development' in this dataset, respectively, correspond to definitions of knowledge 'exploration' and 'exploitation' in the innovation theory literature. We can thus test theories of how equity ownership status relates to innovation strategy. We find that public firms have greater research intensity than private firms, inconsistent with theories asserting private ownership is more conducive to exploration. We also find public firms invest more intensely in innovation of all sorts. These results suggest relaxed financing constraints enjoyed by public firms, as well as their diversified shareholder bases, make them more conducive to investing in all types of innovation. Reconciling several seemingly conflicting results in prior research, we find private-equity-owned firms, though not less innovative overall than other private firms, skew their innovation strategies toward development and away from research.
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Re-engineering Key National Economic Indicators
July 2019
Working Paper Number:
CES-19-22
Traditional methods of collecting data from businesses and households face increasing challenges. These include declining response rates to surveys, increasing costs to traditional modes of data collection, and the difficulty of keeping pace with rapid changes in the economy. The digitization of virtually all market transactions offers the potential for re-engineering key national economic indicators. The challenge for the statistical system is how to operate in this data-rich environment. This paper focuses on the opportunities for collecting item-level data at the source and constructing key indicators using measurement methods consistent with such a data infrastructure. Ubiquitous digitization of transactions allows price and quantity be collected or aggregated simultaneously at the source. This new architecture for economic statistics creates challenges arising from the rapid change in items sold. The paper explores some recently proposed techniques for estimating price and quantity indices in large scale item-level data. Although those methods display tremendous promise, substantially more research is necessary before they will be ready to serve as the basis for the official economic statistics. Finally, the paper addresses implications for building national statistics from transactions for data collection and for the capabilities and organization of the statistical agencies in the 21st century.
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Early-Stage Business Formation: An Analysis of Applications for Employer Identification Numbers
December 2018
Working Paper Number:
CES-18-52
This paper reports on the development and analysis of a newly constructed dataset on the early stages of business formation. The data are based on applications for Employer Identification Numbers (EINs) submitted in the United States, known as IRS Form SS-4 filings. The goal of the research is to develop high-frequency indicators of business formation at the national, state, and local levels. The analysis indicates that EIN applications provide forward-looking and very timely information on business formation. The signal of business formation provided by counts of applications is improved by using the characteristics of the applications to model the likelihood that applicants become employer businesses. The results also suggest that EIN applications are related to economic activity at the local level. For example, application activity is higher in counties that experienced higher employment growth since the end of the Great Recession, and application counts grew more rapidly in counties engaged in shale oil and gas extraction. Finally, the paper provides a description of new public-use dataset, the 'Business Formation Statistics (BFS),' that contains new data series on business applications and formation. The initial release of the BFS shows that the number of business applications in the 3rd quarter of 2017 that have relatively high likelihood of becoming job creators is still far below pre-Great Recession levels.
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New Perspectives on the Decline of U.S.
Manufacturing Employment
April 2018
Working Paper Number:
CES-18-17
We use relatively unexplored dimensions of US microdata to examine how US manufacturing employment has evolved across industries, rms, establishments, and regions. We show that these data provide support for both trade- and technology-based explanations of the overall decline of employment over this period, while also highlighting the di-culties of estimating an overall contribution for each mechanism. Toward that end, we discuss how further analysis of these trends might yield sharper insights.
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State Taxation and the Reallocation of Business Activity: Evidence from Establishment-Level Data
January 2017
Working Paper Number:
CES-17-02
Using Census microdata on multi-state firms, we estimate the impact of state taxes on business activity. For C corporations, employment and the number of establishments have corporate tax elasticities of -0.4, and do not vary with changes in personal tax rates. Pass-through entity activities show tax elasticities of -0.2 to -0.3 with respect to personal tax rates, and are invariant with respect to corporate tax rates. Reallocation of productive resources to other states drives around half the effect. Capital shows similar patterns but is 36% less elastic than labor. The responses are strongest for firms in tradable and footloose industries.
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Creditor Rights and Entrepreneurship:
Evidence from Fraudulent Transfer Law*
January 2016
Working Paper Number:
CES-16-31
We examine entrepreneurial activity following the adoption of fraudulent transfer laws in the U.S. These laws strengthen creditor rights by removing the burden of proof from creditors attempting to claw back funds that were transferred out of failing businesses. These laws are particularly important for entrepreneurs whose personal assets are often commingled with those of the venture. Using establishment-level data from the U.S. Census Bureau, we find significant declines in start-up entry, churning among new entrants, and closures of existing ventures after the passage of these laws. Our
findings suggest that strengthening creditor rights can, in some circumstances, impede entrepreneurial activity and slow down the process of creative destruction.
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