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.
-
Business Applications as a Leading Economic Indicator?
May 2021
Working Paper Number:
CES-21-09R
How are applications to start new businesses related to aggregate economic activity? This paper explores the properties of three monthly business application series from the U.S. Census Bureau's Business Formation Statistics as economic indicators: all business applications, business applications that are relatively likely to turn into new employer businesses ('likely employers'), and the residual series -- business applications that have a relatively low rate of becoming employers ('likely non-employers'). Growth in applications for likely employers significantly leads total nonfarm employment growth and has a strong positive correlation with it. Furthermore, growth in applications for likely employers leads growth in most of the monthly Principal Federal Economic Indicators (PFEIs). Motivated by our findings, we estimate a dynamic factor model (DFM) to forecast nonfarm employment growth over a 12-month period using the PFEIs and the likely employers series. The latter improves the model's forecast, especially in the years following the turning points of the Great Recession and the COVID-19 pandemic. Overall, applications for likely employers are a strong leading indicator of monthly PFEIs and aggregate economic activity, whereas applications for likely non-employers provide early information about changes in increasingly prevalent self-employment activity in the U.S. economy.
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
-
Estimating A Multivariate Arma Model with Mixed-Frequency Data: An Application to Forecasting U.S. GNP at Monthly Intervals
July 1990
Working Paper Number:
CES-90-05
This paper develops and applies a method for directly estimating a multivariate, autoregressive moving-average (ARMA) model with mixed-frequency, time-series data. Unlike standard, single-frequency methods, the method does not require the data to be transformed to a single frequency (by temporally aggregating higher-frequency data to lower frequencies for interpolating lower-frequency data to higher frequencies) or the model to be restricted by frequency. Subject to computational constraints, the method can handle any number of variable and frequencies. In addition, variable can be treated as temporally aggregated and observed with errors and delays. The key to the method is to view lower-frequency data as periodically missing and to use the missing-data variant of the Kalman filter.
In the application, a bivariate, ARMA model is estimated with monthly observations on total employment and quarterly observations on real GNP, in the U.S., for January 1958 to December 1978. The estimated model is, then, used to compute monthly forecasts of the variables for 1 to 12 months ahead, for January 1979 to December 1988. Compared with GNP forecasts, in particular, for similar periods produced by established econometric and time series models, present GNP forecasts are generally more accurate for 1 to 4 months ahead and about equally or slightly less accurate for 5 to 12 months ahead. The application, thus, shows that the present method is tractable and able to effectively exploit cross-frequency sample information, in ARMA estimate and forecasting, which standard methods cannot exploit at all.
View Full
Paper PDF
-
JOB-TO-JOB (J2J) Flows: New Labor Market Statistics From Linked Employer-Employee Data
September 2014
Working Paper Number:
CES-14-34
Flows of workers across jobs are a principal mechanism by which labor markets allocate workers to optimize productivity. While these job flows are both large and economically important, they represent a significant gap in available economic statistics. A soon to be released data product from the U.S. Census Bureau will fill this gap. The Job-to-Job (J2J) flow statistics provide estimates of worker flows across jobs, across different geographic labor markets, by worker and firm characteristics, including direct job-to-job flows as well as job changes with intervening nonemployment. In this paper, we describe the creation of the public-use data product on job-to-job flows. The data underlying the statistics are the matched employer-employee data from the U.S. Census Bureau's Longitudinal Employer-Household Dynamics program. We describe definitional issues and the identification strategy for tracing worker movements between employers in administrative data. We then compare our data with related series and discuss similarities and differences. Lastly, we describe disclosure avoidance techniques for the public use file, and our methodology for estimating national statistics when there is partially missing geography.
View Full
Paper PDF
-
Business Formation: A Tale of Two Recessions
January 2021
Working Paper Number:
CES-21-01
The trajectory of new business applications and transitions to employer businesses differ markedly during the Great Recession and COVID-19 Recession. Both applications and transitions to employer startups decreased slowly but persistently in the post-Lehman crisis period of the Great Recession. In contrast, during the COVID-19 Recession new applications initially declined but have since sharply rebounded, resulting in a surge in applications during 2020. Projected transitions to employer businesses also rise but this is dampened by a change in the composition of applications in 2020 towards applications that are more likely to be nonemployers.
View Full
Paper PDF
-
Public-Use vs. Restricted-Use:
An Analysis Using the American Community Survey
January 2017
Working Paper Number:
CES-17-12
Statistical agencies frequently publish microdata that have been altered to protect confidentiality. Such data retain utility for many types of broad analyses but can yield biased or Insufficiently precise results in others. Research access to de-identified versions of the restricted-use data with little or no alteration is often possible, albeit costly and time-consuming. We investigate the the advantages and disadvantages of public-use and restricted-use data from the American Community
Survey (ACS) in constructing a wage index. The public-use data used were Public Use Microdata Samples, while the restricted-use data were accessed via a Federal Statistical Research Data Center. We discuss the advantages and disadvantages of each data source and compare estimated CWIs and standard errors at the state and labor market levels.
View Full
Paper PDF
-
Early-Stage Business Formation: An Analysis of Applications for Employer Identification Numbers
December 2018
Working Paper Number:
CES-18-52
This paper reports on the development and analysis of a newly constructed dataset on the early stages of business formation. The data are based on applications for Employer Identification Numbers (EINs) submitted in the United States, known as IRS Form SS-4 filings. The goal of the research is to develop high-frequency indicators of business formation at the national, state, and local levels. The analysis indicates that EIN applications provide forward-looking and very timely information on business formation. The signal of business formation provided by counts of applications is improved by using the characteristics of the applications to model the likelihood that applicants become employer businesses. The results also suggest that EIN applications are related to economic activity at the local level. For example, application activity is higher in counties that experienced higher employment growth since the end of the Great Recession, and application counts grew more rapidly in counties engaged in shale oil and gas extraction. Finally, the paper provides a description of new public-use dataset, the 'Business Formation Statistics (BFS),' that contains new data series on business applications and formation. The initial release of the BFS shows that the number of business applications in the 3rd quarter of 2017 that have relatively high likelihood of becoming job creators is still far below pre-Great Recession levels.
View Full
Paper PDF
-
High-Growth Firms in the United States: Key Trends and New Data Opportunities
March 2024
Working Paper Number:
CES-24-11
Using administrative data from the U.S. Census Bureau, we introduce a new public-use database that tracks activities across firm growth distributions over time and by firm and establishment characteristics. With these new data, we uncover several key trends on high-growth firms'critical engines of innovation and economic growth. First, the share of firms that are high-growth has steadily decreased over the past four decades, driven not only by falling firm entry rates but also languishing growth among existing firms. Second, this decline is particularly pronounced among young and small firms, while the share of high-growth firms has been relatively stable among large and old firms. Third, the decline in high-growth firms is found in all sectors, but the information sector has shown a modest rebound beginning in 2010. Fourth, there is significant variation in high-growth firm activity across states, with California, Texas, and Florida having high shares of high-growth firms. We highlight several areas for future research enabled by these new data.
View Full
Paper PDF
-
An Analysis of Key Differences in Micro Data: Results from the Business List Comparison Project
September 2008
Working Paper Number:
CES-08-28
The Bureau of Labor Statistics and the Bureau of the Census each maintain a business register, a universe of all U.S. business establishments and their characteristics, created from independent sources. Both registers serve critical functions such as supplying aggregate data inputs for certain national statistics generated by the Bureau of Economic Analysis. This paper examines key micro-level differences across these two business registers.
View Full
Paper PDF
-
The Role of Industry Classification in the Estimation of Research and Development Expenditures
November 2014
Working Paper Number:
CES-14-45
This paper uses data from the National Science Foundation's surveys on business research and development (R&D) expenditures that have been linked with data from the Census Bureau's Longitudinal Business Database to produce consistent NAICS-based R&D time-series data based on the main product produced by the firm for 1976 to 2008.The results show that R&D spending has shifted away from domestic manufacturing industries in recent years. This is due in part to a shift in U.S. payrolls away from manufacturing establishments for R&D-performing firms.These findings support the notion of an increasingly fragmented production system for R&D-intensive manufacturing firms, whereby U.S. firms control output and provide intellectual property inputs in the form of R&D, but production takes place outside of the firms' U.S. establishments.
View Full
Paper PDF