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Discussion Paper
Electricity Demand as a High-Frequency Economic Indicator: A Case Study of the COVID-19 Pandemic and Hurricane Harvey
Electricity is used by all businesses in the United States. During quickly moving economic shocks—for example, a pandemic or natural disaster—changes in electricity consumption can provide insight to policymakers before traditional survey-based metrics, which can lag weeks or months behind economic conditions and typically only show a snapshot of when the survey was conducted.
Working Paper
Measuring Job Loss during the Pandemic Recession in Real Time with Twitter Data
We present an indicator of job loss derived from Twitter data, based on a fine-tuned neural network with transfer learning to classify if a tweet is job-loss related or not. We show that our Twitter-based measure of job loss is well-correlated with and predictive of other measures of unemployment available in the official statistics and with the added benefits of real-time availability and daily frequency. These findings are especially strong for the period of the Pandemic Recession, when our Twitter indicator continues to track job loss well but where other real-time measures like ...
Discussion Paper
A note on industry concentration measurement
Industry concentration—the share of sales or output accounted for by the largest firms within an industry—has received widespread attention recently, in part because concentration has generally risen in recent decades (figure 1). Measurement challenges are at the core of concentration-based inquiry: industry sales concentration is one of the lowest-frequency business statistics produced by the U.S. statistical agencies, with concentration data being released only twice per decade as part of the Economic Censuses.
Working Paper
Measuring Job Loss during the Pandemic Recession in Real Time with Twitter Data
We present an indicator of job loss derived from Twitter data, based on a fine-tuned neural network with transfer learning to classify if a tweet is job-loss related or not. We show that our Twitter-based measure of job loss is well-correlated with and predictive of other measures of unemployment available in the official statistics and with the added benefits of real-time availability and daily frequency. These findings are especially strong for the period of the Pandemic Recession, when our Twitter indicator continues to track job loss well but where other real-time measures like ...
Working Paper
Measuring Job Loss during the Pandemic Recession in Real Time with Twitter Data
We present an indicator of job loss derived from Twitter data, based on a fine-tuned neural network with transfer learning to classify if a tweet is job-loss related or not. We show that our Twitter-based measure of job loss is well-correlated with and predictive of other measures of unemployment available in the official statistics and with the added benefits of real-time availability and daily frequency. These findings are especially strong for the period of the Pandemic Recession, when our Twitter indicator continues to track job loss well but where other real-time measures like ...
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 ...