Papers Containing Tag(s): 'Department of Energy'
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Timothy R. Wojan - 3
Reed Walker - 3
Viewing papers 1 through 10 of 18
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Working PaperThe Effect of Oil News Shocks on Job Creation and Destruction
January 2025
Working Paper Number:
CES-25-06
Using data from the Annual Survey of Manufactures (ASM) and the Census of Manufacturing (CMF), we construct quarterly measures of job creation and destruction by 3-digit NAICS industries spanning from 1980Q3-2016Q4. These long series allow us to address three questions regarding the effect of oil news shocks. What is the average effect of oil news shocks on sectoral labor reallocation? What characteristics explain the observed heterogeneity in the average responses across industries? Has the response of US manufacturing changed over time? We find evidence that oil news shocks exert only a moderate effect on total manufacturing net employment growth but lead to a significant increase in job reallocation. However, we find a high degree of heterogeneity in responses across industries. We then show that the cross-industry variation in the sensitivity of net employment growth and excess job reallocation to oil news shocks is related to differences in energy costs, the rate of energy to capital expenditures, and the share of mature firms in the industry. Finally, we illustrate how the dynamic response of sectoral job creation and destruction to oil news shocks has declined since the mid-2000s.View Full Paper PDF
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Working PaperExploratory Report: Annual Business Survey Ownership Diversity and Its Association with Patenting and Venture Capital Success
October 2024
Working Paper Number:
CES-24-62
The Annual Business Survey (ABS) as the replacement for the Survey of Business Owners (SBO) serves as the principal data source for investigating business ownership of minorities, women, and immigrants. As a combination of SBO, the innovation questions formerly collected in the Business R&D and Innovation Survey (BRDIS), and an R&D module for microbusinesses with fewer than 10 employees, ABS opens new research opportunities investigating how ownership demographics are associated with innovation. One critical issue that ABS is uniquely able to investigate is the role that diversity among ownership teams plays in facilitating innovation or intermediate innovation outcomes in R&D-performing microbusinesses. Earlier research using ABS identified both demographic and disciplinary diversity as strong correlates to new-to-market innovation. This research investigates the extent to which the various forms of diversity also impact tangible innovation related intermediate outcomes such as the awarding of patents or securing venture capital financing for R&D. The other major difference with the earlier work is the focus on R&D-performing microbusinesses that are an essential input to radical innovation through the division of innovative labor. Evidence that disciplinary and/or demographic diversity affect the likelihood of receiving a patent or securing venture capital financing by small, high-tech start-ups may have implications for higher education, affirmative action, and immigration policy.View Full Paper PDF
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Working PaperGrassroots Design Meets Grassroots Innovation: Rural Design Orientation and Firm Performance
March 2024
Working Paper Number:
CES-24-17
The study of grassroots design'applying structured, creative processes to the usability or aesthetics of a product without input from professional design consultancies'remains under investigated. If design comprises a mediation between people and technology whereby technologies are made more accessible or more likely to delight, then the process by which new grassroots inventions are transformed into innovations valued in markets cannot be fully understood. This paper uses U.S. data on the design orientation of respondents in the 2014 Rural Establishment Innovation Survey linked to longitudinal data on the same firms to examine the association between design, innovation, and employment and payroll growth. Findings from the research will inform questions to be investigated in the recently collected 2022 Annual Business Survey (ABS) that for the first time contains a Design module.View Full Paper PDF
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Working PaperRegistered Report: Exploratory Analysis of Ownership Diversity and Innovation in the Annual Business Survey
March 2023
Working Paper Number:
CES-23-11
A lack of transparency in specification testing is a major contributor to the replicability crisis that has eroded the credibility of findings for informing policy. How diversity is associated with outcomes of interest is particularly susceptible to the production of nonreplicable findings given the very large number of alternative measures applied to several policy relevant attributes such as race, ethnicity, gender, or foreign-born status. The very large number of alternative measures substantially increases the probability of false discovery where nominally significant parameter estimates'selected through numerous though unreported specification tests'may not be representative of true associations in the population. The purpose of this registered report is to: 1) select a single measure of ownership diversity that satisfies explicit, requisite axioms; 2) split the Annual Business Survey (ABS) into an exploratory sample (35%) used in this analysis and a confirmatory sample (65%) that will be accessed only after the publication of this report; 3) regress self-reported new-to-market innovation on the diversity measure along with industry and firm-size controls; 4) pass through those variables meeting precision and magnitude criteria for hypothesis testing using the confirmatory sample; and 5) document the full set of hypotheses to be tested in the final analysis along with a discussion of the false discovery and family-wise error rate corrections to be applied. The discussion concludes with the added value of implementing split sample designs within the Federal Statistical Research Data Center system where access to data is strictly controlled.View Full Paper PDF
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Working PaperExploring 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.View Full Paper PDF
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Working PaperThe Color of Money: Federal vs. Industry Funding of University Research
September 2021
Working Paper Number:
CES-21-26
U.S. universities, which are important producers of new knowledge, have experienced a shift in research funding away from federal and towards private industry sources. This paper compares the effects of federal and private university research funding, using data from 22 universities that include individual-level payments for everyone employed on all grants for each university year and that are linked to patent and Census data, including IRS W-2 records. We instrument for an individual's source of funding with government-wide R&D expenditure shocks within a narrow field of study. We find that a higher share of federal funding causes fewer but more general patents, more high-tech entrepreneurship, a higher likelihood of remaining employed in academia, and a lower likelihood of joining an incumbent firm. Increasing the private share of funding has opposite effects for most outcomes. It appears that private funding leads to greater appropriation of intellectual property by incumbent firms.View Full Paper PDF
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Working PaperDo Cash Windfalls Affect Wages? Evidence from R&D Grants to Small Firms
February 2020
Working Paper Number:
CES-20-06
This paper examines how employee earnings at small firms respond to a cash flow shock in the form of a government R&D grant. We use ranking data on applicant firms, which we link to IRS W2 earnings and other U.S. Census Bureau datasets. In a regression discontinuity design, we find that the grant increases average earnings with a rent-sharing elasticity of 0.07 (0.21) at the employee (firm) level. The beneficiaries are incumbent employees who were present at the firm before the award. Among incumbent employees, the effect increases with worker tenure. The grant also leads to higher employment and revenue, but productivity growth cannot fully explain the immediate effect on earnings. Instead, the data and a grantee survey are consistent with a backloaded wage contract channel, in which employees of financially constrained firms initially accept relatively low wages and are paid more when cash is available.View Full Paper PDF
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Working PaperFile Matching with Faulty Continuous Matching Variables
January 2017
Working Paper Number:
CES-17-45
We present LFCMV, a Bayesian file linking methodology designed to link records using continuous matching variables in situations where we do not expect values of these matching variables to agree exactly across matched pairs. The method involves a linking model for the distance between the matching variables of records in one file and the matching variables of their linked records in the second. This linking model is conditional on a vector indicating the links. We specify a mixture model for the distance component of the linking model, as this latent structure allows the distance between matching variables in linked pairs to vary across types of linked pairs. Finally, we specify a model for the linking vector. We describe the Gibbs sampling algorithm for sampling from the posterior distribution of this linkage model and use artificial data to illustrate model performance. We also introduce a linking application using public survey information and data from the U.S. Census of Manufactures and use LFCMV to link the records.View Full Paper PDF
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Working PaperIndustrial Investments in Energy Efficiency: A Good Idea?
January 2017
Working Paper Number:
CES-17-05
Yes, from an energy-saving perspective. No, once we factor in the negative output and productivity adoption effects. These are the main conclusions we reach by conducting the first large-scale study on cogeneration technology adoption ' a prominent form of energy-saving investments ' in the U.S. manufacturing sector, using a sample that runs from 1982 to 2010 and drawing on multiple data sources from the U.S. Census Bureau and the U.S. Energy Information Administration. We first show through a series of event studies that no differential trends exist in energy consumption nor production activities between adopters and never-adopters prior to the adoption event. We then compute a distribution of realized returns to energy savings, using accounting methods and regression methods, based on our difference-in-difference estimator. We find that (1) significant heterogeneity exists in returns; (2) unlike previous studies in the residential sector, the realized and projected returns to energy savings are roughly consistent in the industrial sector, for both private and social returns; (3) however, cogeneration adoption decreases manufacturing output and productivity persistently for at least the next 7-10 years, relative to the control group. Our IV strategies also show sizable decline in TFP post adoption.View Full Paper PDF
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Working PaperCogeneration Technology Adoption in the U.S.
January 2016
Working Paper Number:
CES-16-30
Well over half of all electricity generated in recent years in Denmark is through cogeneration. In U.S., however, this number is only roughly eight percent. While both the federal and state governments provided regulatory incentives for more cogeneration adoption, the capacity added in the past five years have been the lowest since late 1970s. My goal is to first understand what are and their relative importance of the factors that drive cogeneration technology adoption, with an emphasis on estimating the elasticity of adoption with respect to relative energy input prices and regulatory factors. Very preliminary results show that with a 1 cent increase in purchased electricity price from 6 cents (roughly current average) to 7 cents per kwh, the likelihood of cogeneration technology adoption goes up by about 0.7-1 percent. Then I will try to address the general equilibrium effect of cogeneration adoption in the electricity generation sector as a whole and potentially estimate some key parameters that the social planner would need to determine the optimal cogeneration investment amount. Partial equilibrium setting does not consider the decrease in investment in the utilities sector when facing competition from the distributed electricity generators, and therefore ignore the effects from the change in equilibrium price of electricity. The competitive market equilibrium setting does not consider the externality in the reduction of CO2 emissions, and leads to socially sub-optimal investment in cogeneration. If we were to achieve the national goal to increase cogeneration capacity half of the current capacity by 2020, the US Department of Energy (DOE) estimated an annual reduction of 150 million metric tons of CO2 annually ' equivalent to the emissions from over 25 million cars. This is about five times the annual carbon reduction from deregulation and consolidation in the US nuclear power industry (Davis, Wolfram 2012). Although the DOE estimates could be an overly optimistic estimate, it nonetheless suggests the large potential in the adoption of cogeneration technology.View Full Paper PDF
