Search Results
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.
Discussion Paper
Tracking the Labor Market with "Big Data"
In our research, we explore the information content of the ADP microdata alone by producing an estimate of employment changes independent from the BLS payroll series as well as from other data sources.
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
AI and Coder Employment: Compiling the Evidence
We evaluate whether LLMs have had any discernible impact on the aggregate labor market so far. We focus on occupations that are computer programming-intensive, motivated by data showing that coding is one of the most LLM-exposed tasks. Linking O*NET to CPS we find that aggregate employment of coders has decelerated sharply since the introduction of ChatGPT. Using a novel control variable for industry-level shocks we show that the deceleration is not attributable to the exposure of coders to slowing industries, suggesting instead that coders experienced an occupation-specific shock around ...
Working Paper
Business Exit During the COVID-19 Pandemic: Non-Traditional Measures in Historical Context
Lags in official data releases have forced economists and policymakers to leverage "alternative" or "non-traditional" data to measure business exit resulting from the COVID- 19 pandemic. We first review official data on business exit in recent decades to place the alternative measures of exit within historical context. For the U.S., business exit is countercyclical and fairly common, with about 7.5 percent of firms exiting annually in recent years. Both the high level and the cyclicality of exit are driven by very small firms and establishments. We then explore a range of alternative measures ...
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 ...
Working Paper
LLM on a Budget: Active Knowledge Distillation for Efficient Classification of Large Text Corpora
Large Language Models (LLMs) are highly accurate in classification tasks, however, substantial computational and financial costs hinder their large-scale deployment in dynamic environments. Knowledge Distillation (KD) where a LLM ""teacher"" trains a smaller and more efficient ""student"" model, offers a promising solution to this problem. However, the distillation process itself often remains costly for large datasets, since it requires the teacher to label a vast number of samples while incurring significant token consumption. To alleviate this challenge, in this work we explore the ...