NIST customer demographics: calibrations

This economic analysis brief summarizes the efforts and results of the data collection and analysis of NIST's patent licensee information. Summary statistics for licensees are provided for fiscal years 2006 through 2015. Included in the analysis are demographically descriptive variables such as the age, location, North American Industry Classification System (NAICS) codes, number of employees and geographic distribution of NIST licensees. Overall, this publication provides demographics of companies that licensed NIST technologies.

This economic analysis brief summarizes the efforts and results of the data collection and analysis of NIST's CRADA partner information. Summary statistics for CRADA partners are provided for fiscal years 2006 through 2015. Included in the analysis are demographically descriptive variables such as the age, location, industry NAICS codes, number of employees and geographic distribution of NIST CRADA partners. Overall, these features provide an understanding of the demographic characteristics of companies that collaborate on NIST technologies.

Ecological restoration projects are designed to improve natural and cultural resources. Spending on restoration also stimulates economic impacts to the restoration economy through the creation or support of jobs and business activity. This paper presents accessible methods for quantifying the economic impacts supported by restoration spending and is written to be a guide and toolbox for an interdisciplinary audience of restoration practitioners and economists. Measuring the economic impacts of restoration can be challenging due to lacking or limited data. The complex, collaborative, and heterogeneous nature of restoration projects can make it difficult to clearly track costs, contributing to limited availability and inconsistency in restoration cost data. And business classification systems, such as the North American Industrial Classification System (NAICS), do not include restoration-sectors that consistently describe the patterns of restoration spending. The aims of this paper are to (1) provide restoration practitioners and program managers with a clear understanding of the application of economic impact analyses to restoration, (2) provide a framework for collecting project cost data for economic impact analyses, and (3) provide modeling best practices and an example application of the framework.

ABSTRACTThis article examines the industry classification schemes used in business research from the Standard Industrial Classification system and the North American Industry Classification System to market-based industry classification schemes like the Global Industry Classification and the Industry Classification Benchmark. The examination answers why there are so many, who developed them, and for what purpose. An assortment of industry classification schemes is reviewed to highlight differentiating characteristics, how companies are assigned industry codes, their use by different user groups, and the coverage of industry codes in well-known business databases. The article also introduces the new Sustainability Industry Classification System from the Sustainable Accounting Standards Board designed to classify companies by sustainability metrics.

Although by no means perfect, the benefits data situation for 1929–2006 is much superior to that of the pre-1929 period. Recall that that a gross-earnings rather than regular-earnings concept is used for average hourly earnings (AHE), wherefore average hourly benefits (AHB) are residual in nature (see chapter 1, Gross Earnings versus Regular Earnings). The technique of constructing AHB involves estimating the proportion mark-up of benefits over earnings (PM p , the ratio of benefits to earnings, with subscript p denoting production workers in manufacturing) and then multiplying AHE by this ratio to obtain AHB, which is benefits per work-hour. Sources of benefits data in manufacturing are discussed in chapter 2, Benefits, especially table 2.4. Census data combine production with nonproduction workers and are available only for limited years. Therefore, to construct PM p , one begins with Employer Costs for Employee Compensation (ECEC) series (expressed as dollars per hour worked); but two adjustments to these series are required. First, quarterly wages and benefits are averaged to obtain annual figures (North American Industry Classification System [NAICS], 2004–2006; Standard Industrial Classification System [SIC], 2002–2003). Second, net earnings and total benefits are converted to gross earnings and residual benefits (all workers: SIC, 1986–1988; NAICS, 2004–2006; production workers: SIC, 1988–2003; NAICS, 4Q 2006-3Q 2007).KeywordsPension PlanNorth American Industry Classification SystemEmployer ContributionCompensation BenefitCovered WorkerThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

It is estimated that there are 21.6 million truck trips made each year on Washington state highways. It is further estimated that 45% of transported freight originated from or is destined for a warehouse or distribution center within the state. The growing amount of congestion within the state of Washington has prompted concern over the state’s ability to anticipate and provide for current and future freight transportation infrastructure needs. The objective of this study was to determine freight mobility issues for the Warehouse/Distribution Center industry in Washington State. The scope of this project was primarily two-fold:, to assess the operations of Warehouse/Distribution Centers and to evaluate infrastructure adequacy, and identify deficiencies and investment needs related to transportation. The sample used for this study was compiled from local, county, state, and federal agencies. Collected information was categorized by the Standard Industrial Classification (SIC) or North American Industry Classification System (NAICS) codes associated with industries that operate in the storage and distribution of freight. The data collection method chosen for this study was a mail survey. The survey questionnaire and subsequent analysis were divided into four sections: Warehouse Operations, Inter-modal Systems, International Trade, and Other Transportation Related Issues. The information collected includes the following: 􀂃 Warehouse/distribution center characteristics (hours of operation, square footage of facility, and type of products handled) 􀂃 Daily time distribution of inbound and outbound shipments 􀂃 Mode distribution among inbound and outbound shipments 􀂃 Key routes utilized for inbound and outbound shipments 􀂃 Country of origin and destination of products 􀂃 Mode distribution among imports and exports 􀂃 Ranking of transportation issues 􀂃 Indicators of freight chokepoints The paper provides an analysis and overview of the operations and needs of the warehouse/distribution center industry in the state of Washington.

The Worker Profiling and Reemployment Services (WPRS) system was established nationwide following the 1993 enactment of Public Law 103-152. The law requires state employment security agencies to profile new claimants for regular unemployment insurance (UI) benefits to identify those most likely to exhaust their regular benefits, and refer them to reemployment services to promote a faster transition to new employment. In November 1994, the Michigan Employment Security Commission (MESC) began profiling new UI claimants with technical assistance from the W. E. Upjohn Institute for Employment Research Since WPRS profiling was introduced in Michigan much has changed, but the same model was in use until very recently. The MESC has been abolished, with UI now administered by the Michigan Bureau of Workers' and Unemployment Compensation (MBWUC). The process of taking UI claims has shifted from in-person interviews at local offices around the state to telephone claims taken by staff at three call centers to be located in Detroit, Grand Rapids, and Saginaw. Michigan has also changed from being a wage-request state for UI eligibility determination to a wage-reporting state. This means that each claimant's full benefit year UI entitlement is now known at the time that eligibility is established, a fact that permits new approaches to WPRS modeling. The MBWUC is also switching to using the new Standard Occupation Code (SOC) and North American Industrial Classification System (NAICS). Furthermore, UI has become a partner in new one-stop centers for employment services established in each workforce development area in the state as required by the Workforce Investment Act (WIA) of 1998. To develop a new Michigan WPRS profiling model which is in harmony with the new institutional realities, the MBWUC once again chose to partner with the W.E. Upjohn Institute for Employment Research. This brief paper offers a new WPRS model for Michigan which improves on the original model by applying lessons learned nationwide in the years since WPRS models were first implemented. A variety of alternative specifications were considered, the best of these was proposed as the new Michigan WPRS model. Michigan has since implemented this model and is now using it to profile UI claimants for referral to reemployment services promoting a speedy return to work.

In the modern age of technological innovation, organizations are evolving rapidly due to the abundance of opportunities. This evolution has provided organizations with the ability to leverage technology for their benefit; however, cybersecurity risks have also developed due to the emerging trends. As the software industry faces a surge of emerging trends, it is critical to address the impeding threats to our cybersecurity infrastructure. The computer software industry is classified under the North American Industry Classification System (NAICS) code 511210 for software publishers. The software development market has experienced an exponential growth as a shift to the cloud has rendered a greater need for cutting edge technology, and this trend is projected to continue.

ABSTRACTPaid parental leave is a key factor in promoting gender equity, supporting child development, and fostering paternal involvement. Despite its benefits, the United States (US) lacks a national paid leave policy, with substantial disparities in access and implementation across industries and institutions. To characterize the availability and equity of paid paternity leave policies across US private‐sector companies and academic institutions affiliated with Obstetrics and Gynecology (OB/GYN) residency programs, and to examine disparities by industry. We analyzed publicly reported leave policies using two sources: crowd‐sourced data from US private‐sector companies via theSkimm and publicly available policies OB/GYN residency‐affiliated institutions listed in the American Medical Association's (AMA) Fellowship and Residency Electronic Interactive Database Access (FREIDA) database. Employers were included if they specified the duration of paid parental leave, excluding those with unlimited vacation, missing duration, or no paternity leave data. Employers were grouped by North American Industry Classification System (NAICS) codes. We compared average leave durations and rates of leave equity. Among 565 private‐sector companies, 81.9% offered paid paternity leave. After exclusion, only 45.8% offered equal maternity and paternity leave (n = 498). Fathers received 9.7 weeks on average versus 15.5 for mothers (p < 0.001). Among 240 OB/GYN‐affiliated institutions, 75.7% of those offering paid leave provided equal durations, though paternity leave was shorter than that in the private sector (6.1 vs. 9.7 weeks; p < 0.001). Academic institutions may prioritize equity more than private employers, but offer less generous leave overall. National standards are needed to reduce disparities.

This study compares the properties of the Global Industry Classification Standard (GICS) with three alternatives: Standard Industrial Classification, North American Industry Classification System, and Fama–French classification. First, we demonstrate that GICS results in more reliable industry groupings for financial analysis and research; in particular, we find that estimations of performance-adjusted discretionary accruals (PADA) based on GICS significantly outperform estimates derived using each of the three alternative classifications systems in capturing discretionary accruals. Second, we show that the difference between GICS and the other systems can provide significantly different results, and hence different inferences, in empirical studies that rely on industry classification. Specifically, we revisit findings by Teoh et al. (J Financ 53[6]:1935–1970, 1998a) and assess the conclusion that initial public offering (IPO) issuers with high abnormal accruals during the IPO year experience subsequent poorer long-term stock performance than issuers with low discretionary accruals do. We find that this result disappears when PADA estimates are based on GICS. Our results call for serious consideration of using GICS classifications in research, either in the primary analysis or as a necessary corroboration.

Industry classifications are used by investors, economists, and policy makers for a great variety of purposes. The traditional economic‐activity‐based systems (Global Industry Classification Standard, North American Industry Classification System, Standard Industrial Classification, and Fama–French) have been supplemented in recent years by alternative classification systems. Our purpose is to provide another alternative system that forms classification groups based on the structure of firm financial statements. Using cluster analysis, a multivariate tool that forms groups where their characteristics are similar within groups and distinct across groups, we form clusters of large U.S. firms based on their common‐size financial statements (percentage breakdowns of balance sheets and income statements). We characterize the financial clusters based on their industry classifications and other economic information and assess the ability of financial clusters and industry groups, separately and jointly, to explain stock return correlations of all pairs of firms. Our results demonstrate that using financial clusters and industry groups together proves advantageous relative to using either alone.

Respirable crystalline silica (RCS) can potentially cause silicosis, lung cancer, and renal failure. The current study estimates the percentages of workers potentially overexposed to concentrations of RCS dust and silicosis proportional mortality rates (PMRs) by industry. Occupational Safety and Health Administration compliance inspection sampling data for RCS collected during 1979 to 2015 were used to estimate percentages of workers exposed. The results were used in combination with US Census Bureau estimates to produce industry specific worker population estimates for 2014. Estimates of the numbers and percentages of workers exposed to RCS concentrations at least 1, 2, 5, and 10 times the National Institute for Occupational Safety and Health recommended exposure limit (REL) were calculated by industry using the 2002 North American Industry Classification System. Silicosis PMRs by industry were estimated using National Center for Health Statistics multiple cause of death data. RCS concentrations/workers exposed were highest in the poured concrete foundation and structure contractors; commercial and institutional building construction; and masonry contractors. Approximately 100 000 workers were exposed above the RCS REL, and most (79%) worked in the construction industry. Tile and terrazzo contractors (12%); brick, stone, and related construction merchant wholesalers (10%); masonry contractors (6%) and poured concrete foundation and structure contractors (6%) were the highest percentages of workers potentially overexposed. PMRs were highest for the structural clay product manufacturing and the foundries industries. Percentages of workers exposed to RCS varied by industry and in some industries workers are exposed over 10 times the REL. Exposures can be reduced below the REL by implementing the hierarchy of controls.

Scope 3 emissions are major contributor for emissions of many industries. Since these emissions are indirect/complex in nature, data availability is a major challenge in estimating them. Current methods mostly rely on high level economic transactions with less inclusion of product details and have shortcomings in handling lack of data. In this research, we firstly discussed the benefits/shortcomings of existing methodologies for estimating scope 3 emissions and proposed a Machine Learning (ML) based framework to overcome those shortcomings. In our approach we map the Central Product Classification (CPC) system with International Standard Industry Classification (ISIC) and North American Industry Classification System (NAICS) together to gain higher granularity in scope 3 emission estimation. For ML models implementation, we used the supply chain emission factors based on NAICS codes as targeted variable. Our best performing model, Gradient Boosting Decision Trees, achieved an R² score of 0.9108 and MSE of 0.034 while giving balanced importance to CPC and ISIC codes. Results suggest our ML framework, combined with integrated classifications, can enhance the granularity and predictive accuracy for scope 3 emission factors derived from monetary input-output databases.

The UK will need to decarbonise low temperature industrial waste heat (up to ≈250 °C) to achieve net-zero greenhouse gas emission targets. Industrial waste heat production represents an opportunity for reduction in the use of primary fuels used in the production of commodities. Energy inefficient processes in addition to increased emissions raises the cost of plant operation, – an undesirable scenario for both industrial competitiveness and the environment. Less is known about the quantity and potential applications for recovered low temperature industrial waste heat in UK and the quantification and characterisation of the resource can provide the needed impetus for the development and adoption of green technologies to help achieve the 2050 Net-Zero target. In this work the potential magnitude of the low temperature industrial waste heat resource in the UK is analysed by using sector-level energy intensity values for different industries while drawing on corresponding estimates from previous studies in the USA and by closely mapping the UK Standard Industrial Classification (SIC 2007) against sectors in the USA (NAICS [North American Industry Classification System]). The assessment undertaken finds that the recoverable potential of low temperature waste heat up to ≈250 °C could be up to 83.7% of the total estimated waste heat potential in UK industry. Significant opportunities exist in this low temperature range for waste heat recovery actions at individual sites. Research and Development (R&D) into alternative improved methods for waste heat recovery, storage, and use (WHRSU) technologies could also produce significant positive environmental and industrial impact.

The economic importance of the food industry for different nations can be seen from the amount of revenue generated and jobs created. The aim of this chapter is to provide an overview of the food industry, as one of the largest- manufacturing sectors in North America and other regions. The chapter is divided into 3 sections. 1. Introduction, evidence that food manufacturing is one of the largest of industries, North American industry classification system (NAICS), industry classification codes from other parts of the world. 2. The North American food industry, classification of the food industry, the US Agri-food sector, the food manufacturing sector, the US foodservice sector, the food dollar expenditure, the US food retail sector, Canadian food manufacturing, the US food industry – summary.3. Global food industry, global food and beverage manufacturing, European Union food manufacturing, Brazil. Russia, India, China, Hong Kong, ASEAN countries, Australia & New zealand, global food industry summary and limitations. With 56 reference citations and 4 appendices.