-
Expanding 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
-
Mobility, 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
-
Tracking Firm Use of AI in Real Time: A Snapshot from the Business Trends and Outlook Survey
March 2024
Working Paper Number:
CES-24-16R
Timely and accurate measurement of AI use by firms is both challenging and crucial for understanding the impacts of AI on the U.S. economy. We provide new, real-time estimates of current and expected future use of AI for business purposes based on the Business Trends and Outlook Survey for September 2023 to February 2024. During this period, bi-weekly estimates of AI use rate rose from 3.7% to 5.4%, with an expected rate of about 6.6% by early Fall 2024. The fraction of workers at businesses that use AI is higher, especially for large businesses and in the Information sector. AI use is higher in large firms but the relationship between AI use and firm size is non-monotonic. In contrast, AI use is higher in young firms. Common uses of AI include marketing automation, virtual agents, and data/text analytics. AI users often utilize AI to substitute for worker tasks and equipment/software, but few report reductions in employment due to AI use. Many firms undergo organizational changes to accommodate AI, particularly by training staff, developing new workflows, and purchasing cloud services/storage. AI users also exhibit better overall performance and higher incidence of employment expansion compared to other businesses. The most common reason for non-adoption is the inapplicability of AI to the business.
View Full
Paper PDF
-
Local and National Concentration Trends in Jobs and Sales: The Role of Structural Transformation
November 2023
Working Paper Number:
CES-23-59
National U.S. industrial concentration rose between 1992-2017. Simultaneously, the Herfindhahl Index of local (six-digit-NAICS by county) employment concentration fell. This divergence between national and local employment concentration is due to structural transformation. Both sales and employment concentration rose within industry-by-county cells. But activity shifted from concentrated Manufacturing towards relatively un-concentrated Services. A stronger between-sector shift in employment relative to sales explains the fall in local employment concentration. Had sectoral employment shares remained at their 1992 levels, average local employment concentration would have risen by 9% by 2017 rather than falling by 7%.
View Full
Paper PDF
-
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.
View Full
Paper PDF
-
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.
View Full
Paper PDF
-
Industry Linkages from Joint Production
January 2023
Working Paper Number:
CES-23-02
I develop a theory of joint production to quantify aggregate economies of scope. In US manufacturing data, increased export demand in one industry raises a firm's sales in its other industries that share knowledge inputs like R&D and software. I estimate that knowledge inputs contribute to economies of scope through their scalability and partial non-rivalry within the firm. On average a 10 percent increase in output in one industry lowers prices in other industries by 0.4 percent. Such economies of scope manifest disproportionately among knowledge proximate industries and imply large spillover impacts of recent US-China trade policy on producer prices.
View Full
Paper PDF
-
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.
View Full
Paper PDF
-
Long-Run Adult Socio-economic Outcomes
from In Utero Airborne Lead Exposure
November 2022
Working Paper Number:
CES-22-53
As a neurotoxin, early exposure to lead has long been assumed to affect socioeconomic out-comes well into adulthood. However, the empirical literature documenting such effects has been limited. This study documents the long-term effects of in utero exposure to air lead on adult socio-economic outcomes, including earnings, disabilities, employment, public assistance, and education, using US survey and administrative data. Specifically, we match individuals in the 2000 US Decennial Census and 2001-2014 American Community Surveys to average lead concentrations in the individual's birth county during his/her 9 months in utero. We find a 0.5 'g/m3 decrease in air lead, representing the average 1975-85 change resulting from the passage of the U.S. Clean Air Act, is associated with an increase in earnings of 3.5%, or a present value, at birth, of $21,400 in lifetime earnings. Decomposing this effect, we find greater exposure to lead in utero is associated with an increase in disabilities in adulthood, an increase in receiving public assistance, and a decrease in employment. Looking at effects by sex, long-term effects for girls seem to fall on participation in the formal labor market, whereas for boys it appears to fall more on hours worked. This is the first study to document such long-term effects from lead using US data. We estimate the present value in 2020, from all earnings impacts from 1975 forward, to be $4,230 Billion using a discount rate of 3%. In 2020 alone, the benefits are $252 B, or about 1.2% of GDP. Thus, our estimates imply the Clean Air Act's lead phase out is still returning a national dividend of over 1% every year.
View Full
Paper PDF
-
What Drives Stagnation: Monopsony or Monopoly?
October 2022
Working Paper Number:
CES-22-45
Wages for the vast majority of workers have stagnated since the 1980s while productivity
has grown. We investigate two coexisting explanations based on rising market power: 1. Monopsony, where dominant firms exploit the limited mobility of their own workers to pay lower wages; and 2. Monopoly, where dominant firms charge too high prices for what they sell, which lowers production and the demand for labor, and hence equilibrium wages economy-wide. Using establishment data from the US Census Bureau between 1997 and 2016, we find evidence of both monopoly and monopsony, where the former is rising over this period and the latter is stable. Both contribute to the decoupling of productivity and wage growth, with monopoly being the primary determinant: in 2016 monopoly accounts for 75% of wage stagnation, monopsony for 25%.
View Full
Paper PDF
