Template-Type: ReDIF-Paper 1.0 Author-Name: Hugh Montag Author-Name-First: Hugh Author-Name-Last: Montag Title: On the Welfare Costs of Perceptions Biases Abstract: Are households significantly harmed by inaccurate beliefs about inflation? This paper analyzes two established inflation perceptions biases and evaluates their welfare effects. The first bias is the frequency bias, where households overweight goods that they purchase frequently but are a small share of their consumption basket. In my French sample, I find that households fixate on bread prices. The second bias is that households consistently overestimate the current inflation rate, which I call the level bias in this paper. I estimate the magnitude of these biases using a confidential French household survey. To evaluate the welfare losses of the two biases, I incorporate biased inflation perceptions into a partial-equilibrium model where households save in a single nominal bond subject to inflation risk. The level bias significantly reduces welfare and asset accumulation, while the frequency bias has negligible effects. The welfare loss shrinks if I remove the perceptions bias while keeping the expectations bias, which suggest that inaccurate perceptions can harm households beyond the effect on forecasts. Creation-Date: 2021 File-URL: https://www.bls.gov/osmr/research-papers/2021/pdf/ec210010.pdf File-Format: Application/pdf Number: 535 Handle: RePEc:bls:wpaper:535 Template-Type: ReDIF-Paper 1.0 Author-Name: Michael Dalton Author-Name-First: Michael Author-Name-Last: Dalton Author-Name: Jeffrey A. Groen Author-Name-First: Jeffrey A. Author-Name-Last: Groen Author-Name: Mark A. Loewenstein Author-Name-First: Mark A. Author-Name-Last: Loewenstein Author-Name: David S. Piccone Author-Name-First: David S. Author-Name-Last: Piccone Author-Name: Anne E. Polivka Author-Name-First: Anne E. Author-Name-Last: Polivka Title: The K-Shaped Recovery: Examining the Diverging Fortunes of Workers in the Recovery from the COVID-19 Pandemic using Business and Household Survey Microdata Abstract: This paper examines employment patterns by wage group over the course of the coronavirus pandemic in the United States using microdata from two well-known data sources from the U.S. Bureau of Labor Statistics: the Current Employment Statistics and the Current Population Survey. We find establishments paying the lowest average wages and the lowest wage workers had the steepest decline in employment and experienced the most persistent losses. We disentangle the extent to which the effect observed for low wage workers is due to these workers being concentrated within a few low wage sectors of the economy versus the pandemic affecting low wage workers in a number of sectors across the economy. Our results indicate that the experience of low wage workers is not entirely due to these workers being concentrated in low wage sectors—for many sectors, the lowest wage quintiles in that sector also has had the worst employment outcomes. From April 2020 to May 2021, between 23% and 46% of the decline in employment among the lowest wage establishments was due to within-industry changes. Another important finding is that even for those who remain employed during the pandemic, the probability of becoming part-time for economic reasons increased, especially for low-wage workers. Creation-Date: 2021 File-URL: https://www.bls.gov/osmr/research-papers/2021/pdf/ec210020.pdf File-Format: Application/pdf Number: 536 Handle: RePEc:bls:wpaper:536 Template-Type: ReDIF-Paper 1.0 Author-Name: Josh Klick Author-Name-First: Josh Author-Name-Last: Klick Author-Name: Anya Stockburger Author-Name-First: Anya Author-Name-Last: Stockburger Title: Experimental CPI for lower and higher income households Abstract: This paper examines CPI indexes for subsets of the target population defined by the bottom and top of the income distribution and compares results with the target population. We use data from the Consumer Expenditure Surveys (CE) to construct biennial and monthly market basket shares for groups of respondents based on their reported income, in order to calculate CPIs using modified Laspeyres and Tornqvist formulas respectively. From 2003 to 2018, we find the Laspeyres index for the lowest income quartile population rose faster than the index for all urban consumers. The Laspeyres index for the highest income quartile population rose slower than the index for all urban consumers. Chained CPI indexes for the income quartile populations rose slower than their Laspeyres counterparts. The measure of consumer substitution was lowest for the lowest income quartile population; the difference between the Laspeyres and Tornqvist index for the lowest income quartile population was less than half the difference for all urban consumers. Creation-Date: 2021 File-URL: https://www.bls.gov/osmr/research-papers/2021/pdf/ec210030.pdf File-Format: Application/pdf Number: 537 Handle: RePEc:bls:wpaper:537 Template-Type: ReDIF-Paper 1.0 Author-Name: Kendra Asher Author-Name-First: Kendra Author-Name-Last: Asher Author-Name: John Glaser Author-Name-First: John Author-Name-Last: Glaser Author-Name: Peter B. Meyer Author-Name-First: Peter B. Author-Name-Last: Meyer Author-Name: Jay Stewart Author-Name-First: Jay Author-Name-Last: Stewart Author-Name: Jerin Varghese Author-Name-First: Jerin Author-Name-Last: Varghese Title: How large are revisions to estimates of quarterly labor productivity growth? Abstract: BLS’s estimates of quarterly labor productivity, output per hour worked, are revised because of revisions to source data. Early estimates of hours worked and output are subject to substantial revisions for a variety of reasons. The BLS productivity program produces three regularly scheduled estimates of labor productivity growth: the preliminary estimate, the first revised estimate, and the second revised estimate. We consider revisions to the preliminary and first revised estimate relative to the second revised estimate. Our goal is to develop intervals to help data users better assess the size of these revisions. Most of the revisions result from regularly scheduled updates of source data. We analyze these revisions to get a better understanding of their sources and to determine whether there are any systematic patterns that could be exploited to construct intervals. We find no evidence of trends or systematic patterns that we could exploit. Most notably, the largest revisions to current and prior quarter output coincide with the BEA’s annual revision to GDP. We then consider three alternative methodologies for constructing intervals: modified confidence intervals, model-based intervals, and percentile-based intervals. We argue that the percentile based intervals are preferable, because they are less sensitive to outliers and therefore result in narrower intervals for a given level of statistical confidence. Creation-Date: 2021 File-URL: https://www.bls.gov/osmr/research-papers/2021/pdf/ec210040.pdf File-Format: Application/pdf Number: 538 Handle: RePEc:bls:wpaper:538 Template-Type: ReDIF-Paper 1.0 Author-Name: Michael D. Giandrea Author-Name-First: Michael D. Author-Name-Last: Giandrea Author-Name: Robert J. Kornfeld Author-Name-First: Robert J. Author-Name-Last: Kornfeld Author-Name: Peter B. Meyer Author-Name-First: Peter B. Author-Name-Last: Meyer Author-Name: Susan G. Powers Author-Name-First: Susan G. Author-Name-Last: Powers Title: Alternative Capital Asset Depreciation Rates for U.S. Capital and Multifactor Productivity Measures Abstract: The Bureau of Economic Analysis (BEA) and the Bureau of Labor Statistics (BLS) use estimates of depreciation rates for structures and equipment to construct estimates of capital stock from data on capital investments. The depreciation rates are based mainly on research by Hulten and Wykoff from the early 1980s, and may be out of date. Recent studies by Statistics Canada (2007 and 2015), using Canadian data on used asset transactions from Canada’s Annual Capital Expenditures and Repair Survey (CAPEX) of establishments, found relatively faster depreciation rates, especially for structures. A study by Bokhari and Geltner (2019) used U.S. data on used asset prices and also found faster depreciation rates for structures. To illustrate the potential effects of implementing these estimates from newer studies, we created a concordance to match Canadian to U.S. asset categories, and then re-estimated BEA capital stock measures and the BLS capital and multifactor productivity measures using depreciation rates based on the CAPEX survey. We find that using these faster depreciation rates results in substantially lower estimates of net capital stocks and higher estimates of depreciation in BEA’s accounts, and has minimal effects on growth rates of multifactor productivity (MFP) in the BLS accounts. Creation-Date: 2021 File-URL: https://www.bls.gov/osmr/research-papers/2021/pdf/ec210050.pdf File-Format: Application/pdf Number: 539 Handle: RePEc:bls:wpaper:539 Template-Type: ReDIF-Paper 1.0 Author-Name: Thesia Garner Author-Name-First: Thesia Author-Name-Last: Garner Author-Name: Jake Schild Author-Name-First: Jake Author-Name-Last: Schild Title: Consumer Response to Economic Impact Payments during the COVID-19 Pandemic and the Role of Subjective Assessments of Well-Being: A View from the U.S. Using a Rapid Response Survey Abstract: COVID-19 has become a crisis that is impacting lives, economies, and ways of life around the world. Governments have responded with policies to support and protect their populations, businesses have closed or restricted access, and consumers have adapted as best as they could. Determining in the short-run how well these policies might be working and the socio-economic impact of the pandemic on individuals and households resulted in new data collection efforts worldwide and the greater use of rapid response surveys. This research reports one such effort in the United States (U.S.) to collect data using the Household Pulse Survey (HPS), with a focus on the use of government provided economic impact or stimulus payments by households. These payments were expected to have maximum and immediate impacts. Results reveal that household were most likely to use their economic impact payments to pay off debt as opposed to meeting their spending needs. Respondents who report lower levels of subjective well-being are more likely to use the stimulus payment to "mostly pay off debt" The probability of using the stimulus payment to "mostly pay off debt" increases as subjective assessments of well-being worsen. This research is one of the earliest to examine the role subjective assessments of well-being play in determining consumer response to receipt of economic impact payments during the COVID-19 pandemic. Creation-Date: 2021 File-URL: https://www.bls.gov/osmr/research-papers/2021/pdf/ec210060.pdf File-Format: Application/pdf Number: 540 Handle: RePEc:bls:wpaper:540 Template-Type: ReDIF-Paper 1.0 Author-Name: Cindy Cunningham Author-Name-First: Cindy Author-Name-Last: Cunningham Author-Name: Lucia Foster Author-Name-First: Lucia Author-Name-Last: Foster Author-Name: Cheryl Grim Author-Name-First: Cheryl Author-Name-Last: Grim Author-Name: John Haltiwanger Author-Name-First: John Author-Name-Last: Haltiwanger Author-Name: Sabrina Wulff Pabilonia Author-Name-First: Sabrina Author-Name-Last: Wulff Pabilonia Author-Name: Jay Stewart Author-Name-First: Jay Author-Name-Last: Stewart Author-Name: Zoltan Wolf Author-Name-First: Zoltan Author-Name-Last: Wolf Title: Productivity Dispersion, Entry, and Growth in U.S. Manufacturing Industries Abstract: Within-industry productivity dispersion is pervasive and exhibits substantial variation across countries, industries, and time. We build on prior research that explores the hypothesis that periods of innovation are initially associated with a surge in business start-ups, followed by increased experimentation that leads to rising dispersion potentially with declining aggregate productivity growth, and then a shakeout process that results in higher productivity growth and declining productivity dispersion. Using novel detailed industry-level data on total factor productivity and labor productivity dispersion from the Dispersion Statistics on Productivity along with novel measures of entry rates from the Business Dynamics Statistics and productivity growth data from the Bureau of Labor Statistics for U.S. manufacturing industries, we find support for this hypothesis, especially for the high-tech industries. Creation-Date: 2021 File-URL: https://www.bls.gov/osmr/research-papers/2021/pdf/ec210070.pdf File-Format: Application/pdf Number: 541 Handle: RePEc:bls:wpaper:541 Template-Type: ReDIF-Paper 1.0 Author-Name: Michael Dalton Author-Name-First: Michael Author-Name-Last: Dalton Title: Putting the Paycheck Protection Program into Perspective: An Analysis Using Administrative and Survey Data Abstract: After matching over 3 million loans from the $669 billion Paycheck Protection Program to administrative wage records, I estimate a doubly robust dynamic difference-in-difference event study showing robust, causal impacts of the loans on employment, wages, and opening status of establishments 7 months after PPP approval. Doing back-of-the-envelope calculations, I find a range of $20,000 to $34,000 of PPP spent per employee-month retained, with about 24% of the PPP money going towards wage retention in the baseline model. Small and low-wage establishments show the largest impact from PPP. Creation-Date: 2021 File-URL: https://www.bls.gov/osmr/research-papers/2021/pdf/ec210080.pdf File-Format: Application/pdf Number: 542 Handle: RePEc:bls:wpaper:542 Template-Type: ReDIF-Paper 1.0 Author-Name: Brett Matsumoto Author-Name-First: Brett Author-Name-Last: Matsumoto Title: Producing Quality Adjusted Hospital Price Indexes Abstract: This paper evaluates the relationship between hospital cost and quality and examines the impact of changing hospital quality on measures of hospital price inflation. To construct the official price indexes, government statistical agencies collect the prices of a fixed sample of goods over time. The price indexes can be biased if the quality of the goods is changing over time. In that case, the price index would reflect unobserved quality change in addition to pure price changes. Cost changes associated with the quality change can be used to quality adjust the price index. Measures of hospital quality are published by the Department of Health and Human Services through its Hospital Compare project. This paper estimates the causal relationship between the quality measures and hospital costs using the instrumental variable technique of Doyle et al. (2015), which is based on plausibly exogenous assignment to different ambulance companies. The relationship between cost and quality measures is then used to produce a cost-based quality adjustment for the inpatient hospital producer price index. The quality adjusted inpatient hospital price indexes increase at an average annual rate of 0.19 to 0.26 percentage points less than the unadjusted index from 2010-2016. Creation-Date: 2021 File-URL: https://www.bls.gov/osmr/research-papers/2021/pdf/ec210090.pdf File-Format: Application/pdf Number: 543 Handle: RePEc:bls:wpaper:543 Template-Type: ReDIF-Paper 1.0 Author-Name: Jonathan A. Parker Author-Name-First: Jonathan A. Author-Name-Last: Parker Author-Name: Jake Schild Author-Name-First: Jake Author-Name-Last: Schild Author-Name: Laura Erhard Author-Name-First: Laura Author-Name-Last: Erhard Author-Name: David S. Johnson Author-Name-First: David S. Author-Name-Last: Johnson Title: Household Spending Responses to the Economic Impact Payments of 2020: Evidence from the Consumer Expenditure Survey Abstract: Using the Consumer Expenditure Survey and variation in amount, receipt, and timing of receipt of Economic Impact Payments (EIPs) authorized by the CARES Act, this paper estimates that people spent less of their EIPs in the few months following arrival than in similar previous policy episodes and than estimated by existing studies using other types of data. Accounting for volatility during the pandemic and comparing the consumer spending behavior of broadly similar households, people spent roughly 10 percent (standard error 3.4) of their EIPs on non-durable goods and services in the three months of arrival, with little evidence of additional spending in the subsequent three months or on durable goods. People who report mostly spending their EIPs spent 14.3% (3.7) of their EIPs compared to 5.9% (8.3) and -1.6% (5.0) for those who report mostly paying off debt and saving respectively. People with low liquid wealth and people receiving their EIPs on debit cards spent at higher rates: 21.7% (6.4) and 36.8% (24.6) respectively, with economically larger estimates for total spending. Creation-Date: 2021 File-URL: https://www.bls.gov/osmr/research-papers/2021/pdf/ec210100.pdf File-Format: Application/pdf Number: 544 Handle: RePEc:bls:wpaper:544