Alison Aughinbaugh, J. Groen, M. Loewenstein, D. Rothstein, Hugette Sun
{"title":"Employment, telework, and child remote schooling from February to May 2021: evidence from the National Longitudinal Survey of Youth 1997","authors":"Alison Aughinbaugh, J. Groen, M. Loewenstein, D. Rothstein, Hugette Sun","doi":"10.21916/mlr.2023.5","DOIUrl":"https://doi.org/10.21916/mlr.2023.5","url":null,"abstract":"","PeriodicalId":47215,"journal":{"name":"Monthly Labor Review","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2023-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47583585","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}
Cloud computing services (hosting) is an important component of the Producer Price Index (PPI) for data processing, hosting, and related services. The current approach to changes in cloud services is to estimate a price change between the old and new service that is equal to the average of price changes for similar products in the PPI. This method has limitations because it does not specifically account for changes to characteristics such as microprocessor, memory, and data storage, which are key price-determining characteristics that see rapid technological change. In order to estimate price changes more accurately in this ever-evolving industry, we develop time dummy hedonic models. Our models use publicly available data while future BLS models could instead use confidential respondent data for their estimates. One of the challenges of developing models is accounting for the performance of the microprocessors used in the cloud services. One of the cloud service providers in our dataset has a performance benchmark for their cloud services. We calculate models that use this benchmark and compare them to models that use the characteristics of the microprocessors in order to evaluate the appropriateness of using microprocessor characteristics as explanatory variables in future models. The comparison between these two types of models is critical, because none of the other service providers currently has a microprocessor benchmark. We also use a statistical learning technique to select variables for our models. Finally, we discuss the feasibility of applying these models to the PPI in the future.
{"title":"Exploring quality adjustment in PPI cloud computing","authors":"Steven D. Sawyer, C. O'Bryan","doi":"10.21916/mlr.2023.4","DOIUrl":"https://doi.org/10.21916/mlr.2023.4","url":null,"abstract":"Cloud computing services (hosting) is an important component of the Producer Price Index (PPI) for data processing, hosting, and related services. The current approach to changes in cloud services is to estimate a price change between the old and new service that is equal to the average of price changes for similar products in the PPI. This method has limitations because it does not specifically account for changes to characteristics such as microprocessor, memory, and data storage, which are key price-determining characteristics that see rapid technological change. In order to estimate price changes more accurately in this ever-evolving industry, we develop time dummy hedonic models. Our models use publicly available data while future BLS models could instead use confidential respondent data for their estimates. One of the challenges of developing models is accounting for the performance of the microprocessors used in the cloud services. One of the cloud service providers in our dataset has a performance benchmark for their cloud services. We calculate models that use this benchmark and compare them to models that use the characteristics of the microprocessors in order to evaluate the appropriateness of using microprocessor characteristics as explanatory variables in future models. The comparison between these two types of models is critical, because none of the other service providers currently has a microprocessor benchmark. We also use a statistical learning technique to select variables for our models. Finally, we discuss the feasibility of applying these models to the PPI in the future.","PeriodicalId":47215,"journal":{"name":"Monthly Labor Review","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2023-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48906778","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}
John Bieler, Caleb Sungwoo Cho, JD Gayer, Brett Matsumoto, Brian T. Parker, Daniel Wang
{"title":"Incorporating medical claims data in the Consumer Price Index","authors":"John Bieler, Caleb Sungwoo Cho, JD Gayer, Brett Matsumoto, Brian T. Parker, Daniel Wang","doi":"10.21916/mlr.2023.3","DOIUrl":"https://doi.org/10.21916/mlr.2023.3","url":null,"abstract":"","PeriodicalId":47215,"journal":{"name":"Monthly Labor Review","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42109874","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}
John Bieler, Jonathan D. Church, Kelley W. Khatchadourian, Brian T. Parker, Daniel Wang
Respondents’ low response rates that affect not only sample size but also sample composition have emerged as a central challenge in measuring consumer price change for medical care. In the case of medical care, insufficient responses from respondents have made getting an accurate mix of prices by payer type (self-pay, insurance, Medicare Part B) more difficult. This article summarizes recent methodological improvements to the medical care index that address a decline in response rates over the last two decades and an overrepresentation of self-pay price quotes.
{"title":"Improving response rates and representativity in the CPI medical care index","authors":"John Bieler, Jonathan D. Church, Kelley W. Khatchadourian, Brian T. Parker, Daniel Wang","doi":"10.21916/mlr.2023.2","DOIUrl":"https://doi.org/10.21916/mlr.2023.2","url":null,"abstract":"Respondents’ low response rates that affect not only sample size but also sample composition have emerged as a central challenge in measuring consumer price change for medical care. In the case of medical care, insufficient responses from respondents have made getting an accurate mix of prices by payer type (self-pay, insurance, Medicare Part B) more difficult. This article summarizes recent methodological improvements to the medical care index that address a decline in response rates over the last two decades and an overrepresentation of self-pay price quotes.","PeriodicalId":47215,"journal":{"name":"Monthly Labor Review","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2023-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41731595","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":"The impact of the COVID-19 pandemic on workers with a criminal history","authors":"D. Schwam, S. Bushway, J. Wenger","doi":"10.21916/mlr.2023.1","DOIUrl":"https://doi.org/10.21916/mlr.2023.1","url":null,"abstract":"","PeriodicalId":47215,"journal":{"name":"Monthly Labor Review","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2023-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43779245","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}
Analysts and economists often face the task of using employment metrics to characterize industries of interest. Some key challenges can be understanding where to find employment metrics, the differences in various employment metrics, and when each metric should be used. This article analyzes a variety of publicly available employment data for the United States and compares these data. A detailed description of the intricacies of each data source is provided, which covers factors such as regionality, industry breakout, periodicity, and the types of jobs included. This article provides several case study examples, using the oil and gas extraction, coal mining, and chemical manufacturing sectors to portray challenges data users may face when developing employment estimates that suit their needs. Data users should be aware of a variety of data sources to understand alternative analysis options when data limitations are present and to determine which data source best meets their needs. Instances may occur in which information from one dataset may be used to help impute missing values.
{"title":"Jobs, jobs, jobs: what’s an analyst to do?","authors":"Gavin C. Pickenpaugh, Justin M. Adder","doi":"10.21916/mlr.2022.31","DOIUrl":"https://doi.org/10.21916/mlr.2022.31","url":null,"abstract":"Analysts and economists often face the task of using employment metrics to characterize industries of interest. Some key challenges can be understanding where to find employment metrics, the differences in various employment metrics, and when each metric should be used. This article analyzes a variety of publicly available employment data for the United States and compares these data. A detailed description of the intricacies of each data source is provided, which covers factors such as regionality, industry breakout, periodicity, and the types of jobs included. This article provides several case study examples, using the oil and gas extraction, coal mining, and chemical manufacturing sectors to portray challenges data users may face when developing employment estimates that suit their needs. Data users should be aware of a variety of data sources to understand alternative analysis options when data limitations are present and to determine which data source best meets their needs. Instances may occur in which information from one dataset may be used to help impute missing values.","PeriodicalId":47215,"journal":{"name":"Monthly Labor Review","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2022-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46362814","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":"Empirical evidence for the “Great Resignation”","authors":"V. Amanor-Boadu","doi":"10.21916/mlr.2022.29","DOIUrl":"https://doi.org/10.21916/mlr.2022.29","url":null,"abstract":"","PeriodicalId":47215,"journal":{"name":"Monthly Labor Review","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2022-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49036776","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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