Search Results
Working Paper
Improving the Accuracy of Economic Measurement with Multiple Data Sources: The Case of Payroll Employment Data
This paper combines information from two sources of U.S. private payroll employment to increase the accuracy of real-time measurement of the labor market. The sources are the Current Employment Statistics (CES) from BLS and microdata from the payroll processing firm ADP. We briefly describe the ADP-derived data series, compare it to the BLS data, and describe an exercise that benchmarks the data series to an employment census. The CES and the ADP employment data are each derived from roughly equal-sized samples. We argue that combining CES and ADP data series reduces the measurement error ...
Working Paper
Tracking Real Time Layoffs with SEC Filings: A Preliminary Investigation
We explore a new source of data on layoffs: timely 8-K filings with the Securities and and Exchange Commission. We develop measures of both the number of reported layoff events and the number of affected workers. These series are highly correlated with the business cycle and other layoff indicators. Linking firm-level reported layoff events with WARN notices suggests that 8-K filings are sometimes available before WARN notices, and preliminary regression results suggest our layoff series are useful for forecasting. We also document the industry composition of the data and specific areas ...
Discussion Paper
Measuring AI Uptake in the Workplace
Artificial Intelligence (AI) may be poised to raise productivity across various domains, including writing (Noy and Zhang 2023), programming (Peng et al. 2023), and research and development (Toner-Rodgers 2024; Korinek 2023). However, understanding the extent to which AI—and generative AI in particular—has been adopted as part of the production process remains an open question. This note reviews the extant surveys on AI adoption at both the employee and firm levels. Surveys of firms show a wide spread of adoption rates, ranging from 5 percent to about 40 percent. Surveys of workers show ...
Journal Article
Understanding the evolution of trade deficits: trade elasticities of industrialized countries
In this article, the authors present updated trade elasticities?measures of how much imports and exports change in response to income and price changes?for the U.S. and six other industrialized countries, collectively known as the Group of Seven. They find that the imports and exports of these countries are slightly more responsive to changes in a country?s total income over a period that ends in 2006, compared with a period that ends in 1994.
Working Paper
Business Exit During the COVID-19 Pandemic: Non-Traditional Measures in Historical Context
Given lags in official data releases, economists have studied "alternative data" measures of business exit resulting from the COVID-19 pandemic. Such measures are difficult to understand without historical context, so we review official data on business exit in recent decades. Business exit is common in the U.S., with about 7.5 percent of firms exiting annually in recent years, and is countercyclical (particularly recently). Both the high level and the cyclicality of exit are driven by very small firms. We explore a range of alternative measures and indicators of business exit, including ...
Working Paper
Business Dynamics in the National Establishment Time Series (NETS)/Leland Crane, Ryan Decker
Business microdata have proven useful in a number of fields, but the main sources of comprehensive microdata are subject to significant confidentiality restrictions. A growing number of papers instead use a private data source seeking to cover the universe of U.S. business establishments, the National Establishment Time Series (NETS). Previous research documents the representativeness of NETS in terms of the distribution of employment and establishment counts across industry, geography, and establishment size. But there exists considerable need among researchers for microdata suitable for ...
Working Paper
Total Recall? Evaluating the Macroeconomic Knowledge of Large Language Models
We evaluate the ability of large language models (LLMs) to estimate historical macroeconomic variables and data release dates. We find that LLMs have precise knowledge of some recent statistics, but performance degrades as we go farther back in history. We highlight two particularly important kinds of recall errors: mixing together first print data with subsequent revisions (i.e., smoothing across vintages) and mixing data for past and future reference periods (i.e., smoothing within vintages). We also find that LLMs can often recall individual data release dates accurately, but aggregating ...