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Newsletter
Blending Traditional and Alternative Labor Market Data with CHURN
In this article, we present a new real-time model called CHURN—short for Chicago Fed Unemployment Rate Nowcast. CHURN provides a weekly tracking estimate for the civilian unemployment rate (UR) produced by the U.S. Bureau of Labor Statistics (BLS). To do so, CHURN blends monthly statistics on job flows (i.e., job-finding and job-separation rates) from the BLS and other traditional labor market indicators with alternative high-frequency indicators from private sector sources.
Newsletter
Measuring the relationship between business reopenings, Covid-19, and consumer behavior
On March 17, 2020, seven counties in the San Francisco Bay Area put into place the first stay-at-home orders in the United States. In the following weeks, counties and states implemented a cascading sequence of stay-at-home orders, bans on public gatherings, shutdowns of nonessential businesses, and face mask mandates. But as small businesses began to face financial insolvency, states and counties began easing these restrictions. To evaluate the effectiveness of policies restricting mobility and business activity, it is important to document the effects of reopening businesses on public ...
Working Paper
Forecasting the Economic Effects of AI
We elicit forecasts of how AI will affect the U.S. economy, comparing the beliefs of five groups: academic economists, employees at AI companies, policy researchers focused on AI, highly accurate forecasters, and the general public. The median respondent in each group expects substantial advances in AI capabilities by 2030, small declines in labor force participation consistent with demographic shifts, and an annual GDP growth rate of 2.5%, which exceeds both the typical medium-run (2.0%) and long-run (1.7%) baseline forecasts from government agencies and private-sector forecasters. ...
Working Paper
The Chicago Fed Labor Market Indicators: Bridging the Gap with Alternative Labor Data
We present the Chicago Fed Labor Market Indicators (LMI): a twice-monthly release that includes the job-finding rate, the job-separation rate, and a forecast for the U.S. Bureau of Labor Statistics (BLS) unemployment rate. To overcome limitations in data availability, the LMI uses partial least squares to combine CPS-based finding and separation rates with higher-frequency alternative and traditional labor market data series—such as unemployment insurance claims, Google Trends searches, online job postings, and survey-based indicators. Our resulting flow-consistent unemployment rate (FCR) ...
Working Paper
Do Stay-at-Home Orders Cause People to Stay at Home? Effects of Stay-at-Home Orders on Consumer Behavior
We link the county-level rollout of stay-at-home orders to anonymized cell phone records and consumer spending data. We document three patterns. First, stay-at-home orders caused people to stay at home: County-level measures of mobility declined 9–13% by the day after the stay-at-home order went into effect. Second, stay-at-home orders caused large reductions in spending in sectors associated with mobility: restaurants and retail stores. However, consumers sharply increased spending on food delivery services after orders went into effect. Third, while the response of residents to ...
Working Paper
Heterogeneity in the Marginal Propensity to Consume: Evidence from Covid-19 Stimulus Payments
We identify 16,016 recipients of Covid-19 Economic Impact Payments in anonymized transaction-level debit card data from Facteus. We use an event study framework to show that in the two weeks following a sudden $1,200 payment from the IRS, consumers immediately increased spending by an average of $577, implying a marginal propensity to consume (MPC) of 48%. Consumer spending falls back to normal levels after two weeks. Stimulus recipients who live paycheck-to-paycheck spend 68% of the stimulus payment immediately, while recipients who save much of their monthly income spend 23% of the stimulus ...
Working Paper
The Returns to Public Library Investment
Local governments spend over 12 billion dollars annually funding the operation of 17,000 public libraries in the United States, yet we know little about their effects. We use data describing the near-universe of public libraries to show that public library investment increases children’s attendance at library events by 18%, children’s checkouts of items by 21%, and library visits by 21%. Increases in library use translate into improved test scores in nearby school districts: a $1,000 or greater per-student capital investment in local public libraries increases reading test scores by 0.02 ...
Working Paper
The Returns to Public Library Investment
Local governments spend over 12 billion dollars annually funding the operation of 15,000 public libraries in the United States. This funding supports widespread library use: more than 50% of Americans visit public libraries each year. But despite extensive public investment in libraries, surprisingly little research quantities the effects of public libraries on communities and children. We use data on the near-universe of U.S. public libraries to study the effects of capital spending shocks on library resources, patron usage, student achievement, and local housing prices. We use a dynamic ...
Working Paper
Tracking U.S. Consumers in Real Time with a New Weekly Index of Retail Trade
We create a new weekly index of retail trade that accurately predicts the U.S. Census Bureau's Monthly Retail Trade Survey (MRTS). The index's weekly frequency provides an early snapshot of the MRTS and allows for a more granular analysis of the aggregate consumer response to fast-moving events such as the Covid-19 pandemic. To construct the index, we extract the co-movement in weekly data series capturing credit and debit card transactions, mobility, gasoline sales, and consumer sentiment. To ensure that the index is representative of aggregate retail spending, we implement a novel ...
Working Paper
Tracking U.S. Consumers in Real Time with a New Weekly Index of Retail Trade
We create a new weekly index of retail trade that accurately predicts the U.S. Census Bureau's Monthly Retail Trade Survey (MRTS). The index's weekly frequency provides an early snapshot of the MRTS and allows for a more granular analysis of the aggregate consumer response to fast-moving events such as the Covid-19 pandemic. To construct the index, we extract the co-movement in weekly data series capturing credit and debit card transactions, foot traffic, gasoline sales, and consumer sentiment. To ensure that the index is representative of aggregate retail spending, we implementa novel ...