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Fatal Errors: The Mortality Value of Accurate Weather Forecasts
June 2023
Working Paper Number:
CES-23-30
We provide the first revealed preference estimates of the benefits of routine weather forecasts. The benefits come from how people use advance information to reduce mor tality from heat and cold. Theoretically, more accurate forecasts reduce mortality if and only if mortality risk is convex in forecast errors. We test for such convexity using data on the universe of mortality events and weather forecasts for a twelve-year period in the U.S. Results show that erroneously mild forecasts increase mortality whereas erro neously extreme forecasts do not reduce mortality. Making forecasts 50% more accurate would save 2,200 lives per year. The public would be willing to pay $112 billion to make forecasts 50% more accurate over the remainder of the century, of which $22 billion reflects how forecasts facilitate adaptation to climate change.
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Where Have All the "Creative Talents" Gone?
Employment Dynamics of US Inventors
April 2023
Working Paper Number:
CES-23-17
How are inventors allocated in the US economy and does that allocation affect innovative capacity? To answer these questions, we first build a model where an inventor with a new idea has the possibility to work for an entrant or incumbent firm. Strategic considerations encourage the incumbent to hire the inventor, offering higher wages, and then not implement her idea. We then combine data on 760 thousand U.S. inventors with the LEHD data. We find that when an inventor is hired by an incumbent, their earnings increases by 12.6 percent and their innovative output declines by 6 to 11 percent.
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Building the Census Bureau Index of Economic Activity (IDEA)
March 2023
Working Paper Number:
CES-23-15
The Census Bureau Index of Economic Activity (IDEA) is constructed from 15 of the Census Bureau's primary monthly economic time series. The index is intended to provide a single time series reflecting, to the extent possible, the variation over time in the whole set of component series. The component series provide monthly measures of activity in retail and wholesale trade, manufacturing, construction, international trade, and business formations. Most of the input series are Principal Federal Economic Indicators. The index is constructed by applying the method of principal components analysis (PCA) to the time series of monthly growth rates of the seasonally adjusted component series, after standardizing the growth rates to series with mean zero and variance 1. Similar PCA approaches have been used for the construction of other economic indices, including the Chicago Fed National Activity Index issued by the Federal Reserve Bank of Chicago, and the Weekly Economic Index issued by the Federal Reserve Bank of New York. While the IDEA is constructed from time series of monthly data, it is calculated and published every business day, and so is updated whenever a new monthly value is released for any of its component series. Since release dates of data values for a given month vary across the component series, with slight variations in the monthly release date for any one component series, updates to the index are frequent. It is unavoidably the case that, at almost all updates, some of the component series lack observations for the current (most recent) data month. To address this situation, component series that are one month behind are predicted (nowcast) for the current index month, using a multivariate autoregressive time series model. This report discusses the input series to the index, the construction of the index by PCA, and the nowcasting procedure used. The report then examines some properties of the index and its relation to quarterly U.S. Gross Domestic Product and to some monthly non-Census Bureau economic indicators.
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Registered 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.
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National Experimental Wellbeing Statistics - Version 1
February 2023
Working Paper Number:
CES-23-04
This is the U.S. Census Bureau's first release of the National Experimental Wellbeing Statistics (NEWS) project. The NEWS project aims to produce the best possible estimates of income and poverty given all available survey and administrative data. We link survey, decennial census, administrative, and third-party data to address measurement error in income and poverty statistics. We estimate improved (pre-tax money) income and poverty statistics for 2018 by addressing several possible sources of bias documented in prior research. We address biases from 1) unit nonresponse through improved weights, 2) missing income information in both survey and administrative data through improved imputation, and 3) misreporting by combining or replacing survey responses with administrative information. Reducing survey error substantially affects key measures of well-being: We estimate median household income is 6.3 percent higher than in survey estimates, and poverty is 1.1 percentage points lower. These changes are driven by subpopulations for which survey error is particularly relevant. For house holders aged 65 and over, median household income is 27.3 percent higher and poverty is 3.3 percentage points lower than in survey estimates. We do not find a significant impact on median household income for householders under 65 or on child poverty. Finally, we discuss plans for future releases: addressing other potential sources of bias, releasing additional years of statistics, extending the income concepts measured, and including smaller geographies such as state and county.
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Business Dynamics Statistics for Single-Unit Firms
December 2022
Working Paper Number:
CES-22-57
The Business Dynamics Statistics of Single Unit Firms (BDS-SU) is an experimental data product that provides information on employment and payroll dynamics for each quarter of the year at businesses that operate in one physical location. This paper describes the creation of the data tables and the value they add to the existing Business Dynamics Statistics (BDS) product. We then present some analysis of the published statistics to provide context for the numbers and demonstrate how they can be used to understand both national and local business conditions, with a particular focus on 2020 and the recession induced by the COVID-19 pandemic. We next examine how firms fared in this recession compared to the Great Recession that began in the fourth quarter of 2007. We also consider the heterogenous impact of the pandemic on various industries and areas of the country, showing which types of businesses in which locations were particularly hard hit. We examine business exit rates in some detail and consider why different metro areas experienced the pandemic in different ways. We also consider entry rates and look for evidence of a surge in new businesses as seen in other data sources. We finish by providing a preview of on-going research to match the BDS to worker demographics and show statistics on the relationship between the characteristics of the firm's workers and outcomes such as firm exit and net job creation.
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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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The U.S. Manufacturing Sector's Response to Higher Electricity Prices: Evidence from State-Level Renewable Portfolio Standards
October 2022
Working Paper Number:
CES-22-47
While several papers examine the effects of renewable portfolio standards (RPS) on electricity prices, they mainly rely on state-level data and there has been little research on how RPS policies affect manufacturing activity via their effect on electricity prices. Using plant-level data for the entire U.S. manufacturing sector and all electric utilities from 1992 ' 2015, we jointly estimate the effect of RPS adoption and stringency on plant-level electricity prices and production decisions. To ensure that our results are not sensitive to possible pre-existing differences across manufacturing plants in RPS and non-RPS states, we implement coarsened exact covariate matching. Our results suggest that electricity prices for plants in RPS states averaged about 2% higher than in non-RPS states, notably lower than prior estimates based on state-level data. In response to these higher electricity prices, we estimate that plant electricity usage declined by 1.2% for all plants and 1.8% for energy-intensive plants, broadly consistent with published estimates of the elasticity of electricity demand for industrial users. We find smaller declines in output, employment, and hours worked (relative to the decline in electricity use). Finally, several key RPS policy design features that vary substantially from state-to-state produce heterogeneous effects on plant-level electricity prices.
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An Examination of the Informational Value of Self-Reported Innovation Questions
October 2022
Working Paper Number:
CES-22-46
Self-reported innovation measures provide an alternative means for examining the economic performance of firms or regions. While European researchers have been exploiting the data from the Community Innovation Survey for over two decades, uptake of US innovation data has been much slower. This paper uses a restricted innovation survey designed to differentiate incremental innovators from more far-ranging innovators and compares it to responses in the Annual Survey of Entrepreneurs (ASE) and the Business R&D and Innovation Survey (BRDIS) to examine the informational value of these positive innovation measures. The analysis begins by examining the association between the incremental innovation measure in the Rural Establishment Innovation Survey (REIS) and a measure of the inter-industry buying and selling complexity. A parallel analysis using BRDIS and ASE reveals such an association may vary among surveys, providing additional insight on the informational value of various innovation profiles available in self-reported innovation surveys.
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Rising Markups or Changing Technology?
September 2022
Working Paper Number:
CES-22-38R
Recent evidence suggests the U.S. business environment is changing, with rising market concentration and markups. The most prominent and extensive evidence backs out firm-level markups from the first-order conditions for variable factors. The markup is identified as the ratio of the variable factor's output elasticity to its cost share of revenue. Our analysis starts from this indirect approach, but we exploit a long panel of manufacturing establishments to permit output elasticities to vary to a much greater extent - relative to the existing literature - across establishments within the same industry over time. With our more detailed estimates of output elasticities, the measured increase in markups is substantially dampened, if not eliminated, for U.S. manufacturing. As supporting evidence, we relate differences in the markups' patterns to observable changes in technology (e.g., computer investment per worker, capital intensity, diversification to non-manufacturing) and find patterns in support of changing technology as the driver of those differences.
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