Papers Containing Tag(s): 'Bureau of Economic Analysis'
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Viewing papers 11 through 20 of 223
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Working PaperEntry Costs Rise with Growth
October 2024
Working Paper Number:
CES-24-63
Over time and across states in the U.S., the number of firms is more closely tied to overall employment than to output per worker. In many models of firm dynamics, trade, and growth with a free entry condition, these facts imply that the costs of creating a new firm increase sharply with productivity growth. This increase in entry costs can stem from the rising cost of labor used in entry and weak or negative knowledge spillovers from prior entry. Our findings suggest that productivity-enhancing policies will not induce firm entry, thereby limiting the total impact of such policies on welfare.View Full Paper PDF
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Working PaperSupply Chain Adjustments to Tariff Shocks: Evidence from Firm Trade Linkages in the 2018-2019 U.S. Trade War
August 2024
Working Paper Number:
CES-24-43
We use the 2018-2019 U.S. trade war to examine how supply chains adjustments to a tariff cost shock affect imports and exports. Using confidential firm-trade linked data, we show that the decline in imports of tariffed goods was driven by discontinuations of U.S. buyer'foreign supplier relationships, reduced formation of new relationships, and exits by U.S. firms from import markets altogether. However, tariffed products where imports were concentrated in fewer suppliers had a smaller decline in import growth. We then construct measures of export exposure to import tariffs by linking tariffs paid by importing firms to their exported products. We find that the most exposed products had lower exports in 2018-2019, with most of the impact occurring in 2019.View Full Paper PDF
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Working PaperExpanding the Frontier of Economic Statistics Using Big Data: A Case Study of Regional Employment
July 2024
Working Paper Number:
CES-24-37
Big data offers potentially enormous benefits for improving economic measurement, but it also presents challenges (e.g., lack of representativeness and instability), implying that their value is not always clear. We propose a framework for quantifying the usefulness of these data sources for specific applications, relative to existing official sources. We specifically weigh the potential benefits of additional granularity and timeliness, while examining the accuracy associated with any new or improved estimates, relative to comparable accuracy produced in existing official statistics. We apply the methodology to employment estimates using data from a payroll processor, considering both the improvement of existing state-level estimates, but also the production of new, more timely, county-level estimates. We find that incorporating payroll data can improve existing state-level estimates by 11% based on out-of-sample mean absolute error, although the improvement is considerably higher for smaller state-industry cells. We also produce new county-level estimates that could provide more timely granular estimates than previously available. We develop a novel test to determine if these new county-level estimates have errors consistent with official series. Given the level of granularity, we cannot reject the hypothesis that the new county estimates have an accuracy in line with official measures, implying an expansion of the existing frontier. We demonstrate the practical importance of these experimental estimates by investigating a hypothetical application during the COVID-19 pandemic, a period in which more timely and granular information could have assisted in implementing effective policies. Relative to existing estimates, we find that the alternative payroll data series could help identify areas of the country where employment was lagging. Moreover, we also demonstrate the value of a more timely series.View Full Paper PDF
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Working PaperUrban-Biased Growth: A Macroeconomic Analysis
June 2024
Working Paper Number:
CES-24-33
After 1980, larger US cities experienced substantially faster wage growth than smaller ones. We show that this urban bias mainly reflected wage growth at large Business Services firms. These firms stand out through their high per-worker expenditure on information technology and disproportionate presence in big cities. We introduce a spatial model of investment-specific technical change that can rationalize these patterns. Using the model as an accounting framework, we find that the observed decline in the investment price of information technology capital explains most urban-biased growth by raising the profits of large Business Services firms in big cities.View Full Paper PDF
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Working PaperMobility, Opportunity, and Volatility Statistics (MOVS): Infrastructure Files and Public Use Data
April 2024
Working Paper Number:
CES-24-23
Federal statistical agencies and policymakers have identified a need for integrated systems of household and personal income statistics. This interest marks a recognition that aggregated measures of income, such as GDP or average income growth, tell an incomplete story that may conceal large gaps in well-being between different types of individuals and families. Until recently, longitudinal income data that are rich enough to calculate detailed income statistics and include demographic characteristics, such as race and ethnicity, have not been available. The Mobility, Opportunity, and Volatility Statistics project (MOVS) fills this gap in comprehensive income statistics. Using linked demographic and tax records on the population of U.S. working-age adults, the MOVS project defines households and calculates household income, applying an equivalence scale to create a personal income concept, and then traces the progress of individuals' incomes over time. We then output a set of intermediate statistics by race-ethnicity group, sex, year, base-year state of residence, and base-year income decile. We select the intermediate statistics most useful in developing more complex intragenerational income mobility measures, such as transition matrices, income growth curves, and variance-based volatility statistics. We provide these intermediate statistics as part of a publicly released data tool with downloadable flat files and accompanying documentation. This paper describes the data build process and the output files, including a brief analysis highlighting the structure and content of our main statistics.View Full Paper PDF
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Working PaperThe Rise of Specialized Firms
February 2024
Working Paper Number:
CES-24-06
This paper studies firm diversification over 6-digit NAICS industries in U.S. manufacturing. We find that firms specializing in fewer industries now account for a substantially greater share of production than 40 years ago. This reallocation is a key driver of rising industry concentration. Specialized firms have displaced diversified firms among industry leaders'absent this reallocation concentration would have decreased. We then provide evidence that specialized firms produce higher-quality goods: specialized firms tend to charge higher unit prices and are more insulated against Chinese import competition. Based on our empirical findings, we propose a theory in which growth shifts demand toward specialized, high-quality firms, which eventually increases concentration. We conclude that one should expect rising industry concentration in a growing economy.View Full Paper PDF
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Working PaperCollaborative Micro-productivity Project: Establishment-Level Productivity Dataset, 1972-2020
December 2023
Working Paper Number:
CES-23-65
We describe the process for building the Collaborative Micro-productivity Project (CMP) microdata and calculating establishment-level productivity numbers. The documentation is for version 7 and the data cover the years 1972-2020. These data have been used in numerous research papers and are used to create the experimental public-use data product Dispersion Statistics on Productivity (DiSP).View Full Paper PDF
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Working PaperThe Changing Firm and Country Boundaries of US Manufacturers in Global Value Chains
July 2023
Working Paper Number:
CES-23-38
This paper documents how US firms organize goods production across firm and country boundaries. Most US firms that perform physical transformation tasks in-house using foreign manufacturing plants in 2007 also own US manufacturing plants; moreover manufacturing comprises their main domestic activity. By contrast, 'factoryless goods producers' outsource all physical transformation tasks to arm's-length contractors, focusing their in-house efforts on design and marketing. This distinct firm type is missing from standard analyses of manufacturing, growing in importance, and increasingly reliant on foreign suppliers. Physical transformation 'within-the-firm' thus coincides with substantial physical transformation 'within-the-country,' whereas its performance 'outside-the-firm' often also implies 'outside-the-country.' Despite these differences, factoryless goods producers and firms with foreign and domestic manufacturing plants both employ relatively high shares of US knowledge workers. These patterns call for new models and data to capture the potential for foreign production to support domestic innovation, which US firms leverage around the world.View Full Paper PDF
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Working PaperThe 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.View Full Paper PDF
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Working PaperBuilding 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.View Full Paper PDF
