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Working Paper
Human Capital and Unemployment Dynamics: Why More Educated Workers Enjoy Greater Employment Stability
Why do more educated workers experience lower unemployment rates and lower employment volatility? A closer look at the data reveals that these workers have similar job finding rates, but much lower and less volatile separation rates than their less educated peers. We argue that on-the-job training, being complementary to formal education, is the reason for this pattern. Using a search and matching model with endogenous separations, we show that investments in match-specific human capital reduce the outside option of workers, implying less incentives to separate. The model generates ...
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
Beyond the Streetlight: Economic Measurement in the Division of Research and Statistics at the Federal Reserve
This paper was written for the academic conference held in celebration of the 100th anniversary of the Division of Research and Statistics (R&S) of the Federal Reserve Board. The work of the Federal Reserve turns strongly on empirical efforts to understand the structure and state of the economy, and R&S can be thought of as operating a large factory for discovering and developing data and analytical methods to provide evidence relevant to the mission of the Board. This paper, as signaled by its title, illustrates how the measurement research component of the R&S factory often looks far beyond ...
Working Paper
Manufacturing Sentiment: Forecasting Industrial Production with Text Analysis
This paper examines the link between industrial production and the sentiment expressed in natural language survey responses from U.S. manufacturing firms. We compare several natural language processing (NLP) techniques for classifying sentiment, ranging from dictionary-based methods to modern deep learning methods. Using a manually labeled sample as ground truth, we find that deep learning models partially trained on a human-labeled sample of our data outperform other methods for classifying the sentiment of survey responses. Further, we capitalize on the panel nature of the data to train ...
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 ...
Discussion Paper
Why is Involuntary Part-Time Work Elevated?
Despite substantial improvement in the unemployment rate and several other labor market indicators, the number of Americans involuntarily working part time (also called "part-time for economic reasons") remains unusually high nearly five years into the recovery. In this note, we focus on two questions: 1. What can Current Population Survey (CPS) data on the stocks and flows of involuntary part-time employment say about the underlying reasons for its persistently high rate? And 2. Based on this analysis, what can we expect for the evolution of involuntary part-time work going forward?