Papers Containing Tag(s): 'Bureau of Labor Statistics'
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Viewing papers 61 through 70 of 358
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Working PaperWho's Most Exposed to International Shocks? Estimating Differences in Import Price Sensitivity across U.S. Demographic Groups
March 2023
Working Paper Number:
CES-23-13R
Differences in consumption patterns across demographic groups mean that international price shocks differentially affect such groups. We construct import price indexes for U.S. households that vary by age, race, marital status, education, and urban status. Black households and urban households experienced significantly higher import price inflation from 1996-2018 compared to other groups, such as white households and rural households. Sensitivity to international price shocks varies widely, implying movements in exchange rates and foreign prices, both during our sample and during the Covid-19 pandemic, drove sizable differences in import price inflation ' and total inflation ' across households.View Full Paper PDF
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Working PaperUsing Restricted-Access ACS Data to Examine Economic and Noneconomic Factors of Interstate Migration By Race and Ethnicity
March 2023
Working Paper Number:
CES-23-12
We explore how determinants of internal migration differ between Black non-Hispanics, White non-Hispanics, and Hispanics using micro-level, restricted-use American Community Survey (ACS) data matched to data on attributes of sub-geographies down to the county level. This paper extends the discussion of internal migration in the U.S. by not only observing relationships between economic and noneconomic factors and household-level propensities to migrate, but also how these relationships differ across race and ethnicity within smaller geographies than have been explored in previous literature. We show that when controlling for household and location characteristics, minorities have a lower propensity to migrate than White households and document nuances in the responsiveness of internal migration to individual and locational attributes by racial and ethnic population subgroups.View Full Paper PDF
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Working PaperMethodology on Creating the U.S. Linked Retail Health Clinic (LiRHC) Database
March 2023
Working Paper Number:
CES-23-10
Retail health clinics (RHCs) are a relatively new type of health care setting and understanding the role they play as a source of ambulatory care in the United States is important. To better understand these settings, a joint project by the Census Bureau and National Center for Health Statistics used data science techniques to link together data on RHCs from Convenient Care Association, County Business Patterns Business Register, and National Plan and Provider Enumeration System to create the Linked RHC (LiRHC, pronounced 'lyric') database of locations throughout the United States during the years 2018 to 2020. The matching methodology used to perform this linkage is described, as well as the benchmarking, match statistics, and manual review and quality checks used to assess the resulting matched data. The large majority (81%) of matches received quality scores at or above 75/100, and most matches were linked in the first two (of eight) matching passes, indicating high confidence in the final linked dataset. The LiRHC database contained 2,000 RHCs and found that 97% of these clinics were in metropolitan statistical areas and 950 were in the South region of the United States. Through this collaborative effort, the Census Bureau and National Center for Health Statistics strive to understand how RHCs can potentially impact population health as well as the access and provision of health care services across the nation.View Full Paper PDF
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Working PaperIs Affirmative Action in Employment Still Effective in the 21st Century?
November 2022
Working Paper Number:
CES-22-54
We study Executive Order 11246, an employment-based affirmative action policy tar geted at firms holding contracts with the federal government. We find this policy to be in effective in the 21st century, contrary to the positive effects found in the late 1900s (Miller, 2017). Our novel dataset combines data on federal contract acquisition and enforcement with US linked employer-employee Census data 2000'2014. We employ an event study around firms' acquiring a contract, based on Miller (2017), and find the policy had no ef fect on employment shares or on hiring, for any minority group. Next, we isolate the impact of the affirmative action plan, which is EO 11246's preeminent requirement that applies to firms with contracts over $50,000. Leveraging variation from this threshold in an event study and regression discontinuity design, we find similarly null effects. Last, we show that even randomized audits are not effective, suggesting weak enforcement. Our results highlight the importance of the recent budget increase for the enforcement agency, as well as recent policies enacted to improve complianceView Full Paper PDF
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Working PaperLEHD Snapshot Documentation, Release S2021_R2022Q4
November 2022
Working Paper Number:
CES-22-51
The Longitudinal Employer-Household Dynamics (LEHD) data at the U.S. Census Bureau is a quarterly database of linked employer-employee data covering over 95% of employment in the United States. These data are used to produce a number of public-use tabulations and tools, including the Quarterly Workforce Indicators (QWI), LEHD Origin-Destination Employment Statistics (LODES), Job-to-Job Flows (J2J), and Post-Secondary Employment Outcomes (PSEO) data products. Researchers on approved projects may also access the underlying LEHD microdata directly, in the form of the LEHD Snapshot restricted-use data product. This document provides a detailed overview of the LEHD Snapshot as of release S2021_R2022Q4, including user guidance, variable codebooks, and an overview of the approvals needed to obtain access. Updates to the documentation for this and future snapshot releases will be made available in HTML format on the LEHD website.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 PaperOpening the Black Box: Task and Skill Mix and Productivity Dispersion
September 2022
Working Paper Number:
CES-22-44
An important gap in most empirical studies of establishment-level productivity is the limited information about workers' characteristics and their tasks. Skill-adjusted labor input measures have been shown to be important for aggregate productivity measurement. Moreover, the theoretical literature on differences in production technologies across businesses increasingly emphasizes the task content of production. Our ultimate objective is to open this black box of tasks and skills at the establishment-level by combining establishment-level data on occupations from the Bureau of Labor Statistics (BLS) with a restricted-access establishment-level productivity dataset created by the BLS-Census Bureau Collaborative Micro-productivity Project. We take a first step toward this objective by exploring the conceptual, specification, and measurement issues to be confronted. We provide suggestive empirical analysis of the relationship between within-industry dispersion in productivity and tasks and skills. We find that within-industry productivity dispersion is strongly positively related to within-industry task/skill dispersion.View Full Paper PDF
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Working PaperTrade Liberalization and Labor-Market Outcomes: Evidence from US Matched Employer-Employee Data
September 2022
Working Paper Number:
CES-22-42
We use matched employer-employee data to examine outcomes among workers initially employed within and outside manufacturing after trade liberalization with China. We find that exposure to this shock operates predominantly through workers' counties (versus industries), that larger own industry and downstream exposure typically reduce relative earnings, and that greater upstream exposure often raises them. The latter is particularly important outside manufacturing: while we find substantial and persistent predicted declines in relative earnings among manufacturing workers, those outside manufacturing are generally predicted to experience relative earnings gains. Investigation of employment reactions indicates they account for a small share of the earnings effect.View Full Paper PDF
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Working PaperMultinational Firms in the U.S. Economy: Insights from Newly Integrated Microdata
September 2022
Working Paper Number:
CES-22-39
This paper describes the construction of two confidential crosswalk files enabling a comprehensive identification of multinational rms in the U.S. economy. The effort combines firm-level surveys on direct investment conducted by the U.S. Bureau of Economic Analysis (BEA) and the U.S. Census Bureau's Business Register (BR) spanning the universe of employer businesses from 1997 to 2017. First, the parent crosswalk links BEA firm-level surveys on U.S. direct investment abroad and the BR. Second, the affiliate crosswalk links BEA firm-level surveys on foreign direct investment in the United States and the BR. Using these newly available links, we distinguish between U.S.- and foreign-owned multinational firms and describe their prevalence and economic activities in the national economy, by sector, and by geography.View Full Paper PDF
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Working PaperRising 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.View Full Paper PDF
