In September 2020, the U.S. Bureau of Labor Statistics (BLS) published its 2019-29 employment projections. Because these projections did not reflect the potential longterm impacts of the coronavirus disease 2019 (COVID-19) pandemic, BLS developed alternate 2019-29 projections in early 2021, capturing those impacts for selected industries and occupations. These latter projections, based on two alternate pandemic scenarios (moderate impact and strong impact), were followed (in September 2021) by BLS projections for the 2020-30 decade. The present article compares target-year employment levels across these successive sets of projections, focusing on the industries and occupations identified in the alternate projections and discussing relevant developments associated with the COVID-19 pandemic.
{"title":"Expected pandemic-driven employment changes: a comparison of 2019–29 and 2020–30 projection sets","authors":"L. Ice, Michael Rieley","doi":"10.21916/mlr.2022.5","DOIUrl":"https://doi.org/10.21916/mlr.2022.5","url":null,"abstract":"In September 2020, the U.S. Bureau of Labor Statistics (BLS) published its 2019-29 employment projections. Because these projections did not reflect the potential longterm impacts of the coronavirus disease 2019 (COVID-19) pandemic, BLS developed alternate 2019-29 projections in early 2021, capturing those impacts for selected industries and occupations. These latter projections, based on two alternate pandemic scenarios (moderate impact and strong impact), were followed (in September 2021) by BLS projections for the 2020-30 decade. The present article compares target-year employment levels across these successive sets of projections, focusing on the industries and occupations identified in the alternate projections and discussing relevant developments associated with the COVID-19 pandemic.","PeriodicalId":47215,"journal":{"name":"Monthly Labor Review","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2022-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41935706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
D. M. Brodersen, Lisa Boily, G. Paulin, C. Gillham
As a result of economic stressors experienced by vulnerable populations during the Great Recession of 2007–09, participation in the Supplemental Nutrition Assistance Program (SNAP)—the nation’s largest food assistance program—nearly doubled from 2006 to a postrecession peak in 2013. Drawing on data from the 2006 to 2015 U.S. Bureau of Labor Statistics Consumer Expenditure Diary Survey, this article compares SNAP-recipient households to non-SNAP recipient households in the New York area and the United States as a whole for the period before, during, and after the recession. Among the major findings, this study shows substantial differences in characteristics between SNAP and non-SNAP households, including rates of renting, percentage of bachelor’s degree holders, and levels of weekly food-at-home expenditures. The regression analysis shows that food-at-home expenditures remain stable over the business cycle. SNAP participation is positively associated with the probability of making weekly food shopping trips and with an increase in the amount spent per trip nationwide, whereas in the local area, the differences are not significant. Further analysis shows that an income increase from SNAP benefits or other sources results in relatively small increases in food-at-home expenditures.
{"title":"SNAP participation and food-at-home expenditures through the Great Recession: United States and the New York Area","authors":"D. M. Brodersen, Lisa Boily, G. Paulin, C. Gillham","doi":"10.21916/mlr.2022.4","DOIUrl":"https://doi.org/10.21916/mlr.2022.4","url":null,"abstract":"As a result of economic stressors experienced by vulnerable populations during the Great Recession of 2007–09, participation in the Supplemental Nutrition Assistance Program (SNAP)—the nation’s largest food assistance program—nearly doubled from 2006 to a postrecession peak in 2013. Drawing on data from the 2006 to 2015 U.S. Bureau of Labor Statistics Consumer Expenditure Diary Survey, this article compares SNAP-recipient households to non-SNAP recipient households in the New York area and the United States as a whole for the period before, during, and after the recession. Among the major findings, this study shows substantial differences in characteristics between SNAP and non-SNAP households, including rates of renting, percentage of bachelor’s degree holders, and levels of weekly food-at-home expenditures. The regression analysis shows that food-at-home expenditures remain stable over the business cycle. SNAP participation is positively associated with the probability of making weekly food shopping trips and with an increase in the amount spent per trip nationwide, whereas in the local area, the differences are not significant. Further analysis shows that an income increase from SNAP benefits or other sources results in relatively small increases in food-at-home expenditures.","PeriodicalId":47215,"journal":{"name":"Monthly Labor Review","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2022-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45470178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Generational cohorts in the United States have long interested demographers, social scientists, researchers, and others. In 2019, millennials were the same ages—23 to 38 years—as members of Generation X in 2003. I use American Time Use Survey data from these years to examine how millennials in 2019 and members of Generation X in 2003 spent their time on an average day. Millennials were less likely to spend time doing housework, caring for and helping household members, and purchasing goods and services than were members of Generation X. On the other hand, millennials were more likely to spend time providing animal and pet care than were members of Generation X. Each generation spent nearly the same amount of time working on an average day, and the men of each generation spent more time working than their female counterparts. Each generation spent nearly the same amount of time in leisure and sports activities overall. However, millennial men spent more time playing games on an average day than did men in Generation X, and millennial women spent less time reading for leisure than did women in Generation X.
{"title":"Time use of millennials and Generation X: differences across time","authors":"M. Freeman","doi":"10.21916/mlr.2022.3","DOIUrl":"https://doi.org/10.21916/mlr.2022.3","url":null,"abstract":"Generational cohorts in the United States have long interested demographers, social scientists, researchers, and others. In 2019, millennials were the same ages—23 to 38 years—as members of Generation X in 2003. I use American Time Use Survey data from these years to examine how millennials in 2019 and members of Generation X in 2003 spent their time on an average day. Millennials were less likely to spend time doing housework, caring for and helping household members, and purchasing goods and services than were members of Generation X. On the other hand, millennials were more likely to spend time providing animal and pet care than were members of Generation X. Each generation spent nearly the same amount of time working on an average day, and the men of each generation spent more time working than their female counterparts. Each generation spent nearly the same amount of time in leisure and sports activities overall. However, millennial men spent more time playing games on an average day than did men in Generation X, and millennial women spent less time reading for leisure than did women in Generation X.","PeriodicalId":47215,"journal":{"name":"Monthly Labor Review","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2022-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47775055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
U.S. manufacturing employment has been declining for 40 years, but this trend has not affected all regions equally. This paper examines employment changes in the Western region since 1990 to identify trends in the basket of subindustries unique to the region.
{"title":"Western manufacturing employment: trends and peaks over 30 years","authors":"Christopher Nesseth","doi":"10.21916/mlr.2022.2","DOIUrl":"https://doi.org/10.21916/mlr.2022.2","url":null,"abstract":"U.S. manufacturing employment has been declining for 40 years, but this trend has not affected all regions equally. This paper examines employment changes in the Western region since 1990 to identify trends in the basket of subindustries unique to the region.","PeriodicalId":47215,"journal":{"name":"Monthly Labor Review","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2022-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45351672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article employs circularity and unity tests, as well as multilateral index comparisons, to measure the existence and extent of “drift” in the Chained Consumer Price Index for All Urban Consumers (C-CPI-U). It applies various formulas to real data that were used to calculate the Consumer Price Index during the period from December 1999 to December 2017. Overall, the findings show only small amounts of chain drift in the C-CPI-U over the study period.
{"title":"“Chain drift” in the Chained Consumer Price Index: 1999–2017","authors":"R. Cage, B. Williams, Jonathan D. Church","doi":"10.21916/mlr.2021.27","DOIUrl":"https://doi.org/10.21916/mlr.2021.27","url":null,"abstract":"This article employs circularity and unity tests, as well as multilateral index comparisons, to measure the existence and extent of “drift” in the Chained Consumer Price Index for All Urban Consumers (C-CPI-U). It applies various formulas to real data that were used to calculate the Consumer Price Index during the period from December 1999 to December 2017. Overall, the findings show only small amounts of chain drift in the C-CPI-U over the study period.","PeriodicalId":47215,"journal":{"name":"Monthly Labor Review","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2021-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45742229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Statistics on gross job gains and losses, which show the dynamics of job creation and destruction, are now often used by economists and policymakers in understanding the labor market. Available longitudinal microrecord data on employers and employees have allowed researchers to observe, in detail, how employment growth is generated by a continuous stream of job gains and losses across all industries, geographies, and firms of different sizes and ages. Since its first publication in 2003, the Business Employment Dynamics (BED) program at the U.S. Bureau of Labor Statistics (BLS) has expanded greatly, publishing valuable data series in response to policy and research needs for data on employment growth and labor turnover. Currently available data series on employment dynamics and some entrepreneurship indicators allow economists and policymakers to gain a better understanding of the overall labor market and the specific nature and magnitude of job creation and destruction.1
{"title":"Business employment dynamics by wage class","authors":"Akbar Sadeghi, Kevin Cooksey","doi":"10.21916/mlr.2021.25","DOIUrl":"https://doi.org/10.21916/mlr.2021.25","url":null,"abstract":"Statistics on gross job gains and losses, which show the dynamics of job creation and destruction, are now often used by economists and policymakers in understanding the labor market. Available longitudinal microrecord data on employers and employees have allowed researchers to observe, in detail, how employment growth is generated by a continuous stream of job gains and losses across all industries, geographies, and firms of different sizes and ages. Since its first publication in 2003, the Business Employment Dynamics (BED) program at the U.S. Bureau of Labor Statistics (BLS) has expanded greatly, publishing valuable data series in response to policy and research needs for data on employment growth and labor turnover. Currently available data series on employment dynamics and some entrepreneurship indicators allow economists and policymakers to gain a better understanding of the overall labor market and the specific nature and magnitude of job creation and destruction.1","PeriodicalId":47215,"journal":{"name":"Monthly Labor Review","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41738330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Consumer Expenditure Surveys (CE) program of the U.S. Bureau of Labor Statistics (BLS) sponsors nationwide household weekly diary and monthly interview surveys each year for the purpose of producing nationally representative estimates of expenditures, income, and demographics.1 The surveys’ main objective is to measure the spending patterns of consumers living in the United States. The surveys are the only federal government data collection efforts that provide information on the complete range of consumers’ expenditures, as well as their income and demographic characteristics. Similar to other large scale federal survey programs, the BLS CE program aggregates the survey data for a primary purpose, which is to provide estimates critical to the Consumer Price Index (CPI), a Principal Federal Economic Indicator.
{"title":"Assessing Consumer Expenditure Surveys data quality through the lens of data use","authors":"Adam Safir, Dan S. Dorfman","doi":"10.21916/mlr.2021.24","DOIUrl":"https://doi.org/10.21916/mlr.2021.24","url":null,"abstract":"The Consumer Expenditure Surveys (CE) program of the U.S. Bureau of Labor Statistics (BLS) sponsors nationwide household weekly diary and monthly interview surveys each year for the purpose of producing nationally representative estimates of expenditures, income, and demographics.1 The surveys’ main objective is to measure the spending patterns of consumers living in the United States. The surveys are the only federal government data collection efforts that provide information on the complete range of consumers’ expenditures, as well as their income and demographic characteristics. Similar to other large scale federal survey programs, the BLS CE program aggregates the survey data for a primary purpose, which is to provide estimates critical to the Consumer Price Index (CPI), a Principal Federal Economic Indicator.","PeriodicalId":47215,"journal":{"name":"Monthly Labor Review","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2021-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43551878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The term “Indian Country” is often used to mean either the demographic group of Native Americans in the United States or the geographic, tribal communities in which many Native Americans live. This double meaning has led to various treatments of the socioeconomic measures describing Indian Country. Unfortunately, some of these treatments can potentially lead to inaccurate or misleading analyses of Indian Country, for two reasons. First, because socioeconomic data on Indian Country are sparse, analysts frequently do not have the ideal data for their studies, and they have to make do with the only information they can obtain that is close to the concept being analyzed. Second, some previous studies have already mistakenly “mixed apples and oranges” with regard to Indian Country data (as suggested above) and, in so doing, have set a precedent for others to follow. This article addresses this problem by offering a reality check on the alternative definitions of Indian Country and on how different they truly are. The article then provides a taxonomy of these definitions, offering guidance on when they should be applied in efforts to promote the most accurate and reliable findings possible.
{"title":"Alternative measurements of Indian Country: understanding their implications for economic, statistical, and policy analysis","authors":"S. Payson","doi":"10.21916/mlr.2021.23","DOIUrl":"https://doi.org/10.21916/mlr.2021.23","url":null,"abstract":"The term “Indian Country” is often used to mean either the demographic group of Native Americans in the United States or the geographic, tribal communities in which many Native Americans live. This double meaning has led to various treatments of the socioeconomic measures describing Indian Country. Unfortunately, some of these treatments can potentially lead to inaccurate or misleading analyses of Indian Country, for two reasons. First, because socioeconomic data on Indian Country are sparse, analysts frequently do not have the ideal data for their studies, and they have to make do with the only information they can obtain that is close to the concept being analyzed. Second, some previous studies have already mistakenly “mixed apples and oranges” with regard to Indian Country data (as suggested above) and, in so doing, have set a precedent for others to follow. This article addresses this problem by offering a reality check on the alternative definitions of Indian Country and on how different they truly are. The article then provides a taxonomy of these definitions, offering guidance on when they should be applied in efforts to promote the most accurate and reliable findings possible.","PeriodicalId":47215,"journal":{"name":"Monthly Labor Review","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2021-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43676211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Using data from the Current Employment Statistics program, this article explores manufacturing employment dynamics between 1990 and 2019 in the Midwest region of the United States. The article compares and contrasts employment trends for both the region as a whole and the individual states that comprise it. Additionally, the article presents an examination of selected detailed industries. For context, the article uses periods within historical business cycles to frame analysis of manufacturing employment trends.
{"title":"Exploring Midwest manufacturing employment from 1990 to 2019","authors":"Scott Arden, C. Decarlo","doi":"10.21916/mlr.2021.22","DOIUrl":"https://doi.org/10.21916/mlr.2021.22","url":null,"abstract":"Using data from the Current Employment Statistics program, this article explores manufacturing employment dynamics between 1990 and 2019 in the Midwest region of the United States. The article compares and contrasts employment trends for both the region as a whole and the individual states that comprise it. Additionally, the article presents an examination of selected detailed industries. For context, the article uses periods within historical business cycles to frame analysis of manufacturing employment trends.","PeriodicalId":47215,"journal":{"name":"Monthly Labor Review","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2021-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41447026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Employment in rail transportation heads downhill between November 2018 and December 2020","authors":"R. Ansell","doi":"10.21916/mlr.2021.21","DOIUrl":"https://doi.org/10.21916/mlr.2021.21","url":null,"abstract":"","PeriodicalId":47215,"journal":{"name":"Monthly Labor Review","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2021-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43095102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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