Hybrid time series data often require special care in estimating seasonal factors. Series such as the state and metro area Current Employment Statistics produced by the U.S. Bureau of Labor Statistics (BLS) are composed of two different source series that often have two different seasonal patterns. In this paper we address the process to test for differing seasonal patterns within the hybrid series. We also discuss how to apply differing seasonal factors to the separate parts of the hybrid series. Currently, for state employment data, the BLS simply juxtaposes the two different sets of seasonal factors at the transition point between the benchmark part of the data and the survey part. We argue that the seasonal factors should be extrapolated at the transition point or that an adjustment should be made to the level of the unadjusted data to correct for a bias in the survey part of the data caused by differing seasonal factors at the transition month.
{"title":"Seasonal adjustment of hybrid time series: An application to U.S. regional jobs data","authors":"K. Phillips, Jianguo Wang","doi":"10.3233/JEM-160428","DOIUrl":"https://doi.org/10.3233/JEM-160428","url":null,"abstract":"Hybrid time series data often require special care in estimating seasonal factors. Series such as the state and metro area Current Employment Statistics produced by the U.S. Bureau of Labor Statistics (BLS) are composed of two different source series that often have two different seasonal patterns. In this paper we address the process to test for differing seasonal patterns within the hybrid series. We also discuss how to apply differing seasonal factors to the separate parts of the hybrid series. Currently, for state employment data, the BLS simply juxtaposes the two different sets of seasonal factors at the transition point between the benchmark part of the data and the survey part. We argue that the seasonal factors should be extrapolated at the transition point or that an adjustment should be made to the level of the unadjusted data to correct for a bias in the survey part of the data caused by differing seasonal factors at the transition month.","PeriodicalId":53705,"journal":{"name":"Journal of Economic and Social Measurement","volume":"41 1","pages":"191-202"},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/JEM-160428","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70046152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this study we examine the regression-based ratio-correlation method and suggest some new tools for assessing the magnitude and impact of coefficient instability on population estimation errors. We use a robust sample of 904 counties from 11 states and find that: (1) coefficient instability is not a universal source of error in regression models for population estimation and its impact is less than commonly assumed; (2) coefficient instability is not related to bias, but it does decrease precision and increase the allocation error of population estimates; and (3) unstable coefficients have the greatest impact on counties under 20,000 in population size. Our findings suggest that information about the conditions that affect coefficient instability and its impact on estimation error might lead to more targeted and efficient approaches for improving population estimates developed from regression models.
{"title":"New insights on the impact of coefficient instability on ratio-correlation population estimates","authors":"J. Tayman, David A. Swanson","doi":"10.3233/JEM-160422","DOIUrl":"https://doi.org/10.3233/JEM-160422","url":null,"abstract":"In this study we examine the regression-based ratio-correlation method and suggest some new tools for assessing the magnitude and impact of coefficient instability on population estimation errors. We use a robust sample of 904 counties from 11 states and find that: (1) coefficient instability is not a universal source of error in regression models for population estimation and its impact is less than commonly assumed; (2) coefficient instability is not related to bias, but it does decrease precision and increase the allocation error of population estimates; and (3) unstable coefficients have the greatest impact on counties under 20,000 in population size. Our findings suggest that information about the conditions that affect coefficient instability and its impact on estimation error might lead to more targeted and efficient approaches for improving population estimates developed from regression models.","PeriodicalId":53705,"journal":{"name":"Journal of Economic and Social Measurement","volume":"41 1","pages":"121-143"},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/JEM-160422","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70045593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A frequent criticism of GDP states that events that obviously reduce welfare of people can nevertheless increase GDP per capita. We use data of natural disasters as quasi experiments to examine whether alternatives to GDP (Human Development Index, Progress Index, Index of Economic Well-Being and a Happiness Index) lead to more plausible responses to disasters. Applying a Differences-in-Differences approach and estimates from various panels of countries we find no noteworthy differences between the response of real GDP per capita and the responses of suggested alternative welfare measures to a natural disaster except for the Human Development Index.
{"title":"Alternatives to GDP - Measuring the impact of natural disasters using panel data","authors":"Jörg Döpke, Philip Maschke","doi":"10.3233/JEM-160429","DOIUrl":"https://doi.org/10.3233/JEM-160429","url":null,"abstract":"A frequent criticism of GDP states that events that obviously reduce welfare of people can nevertheless increase GDP per capita. We use data of natural disasters as quasi experiments to examine whether alternatives to GDP (Human Development Index, Progress Index, Index of Economic Well-Being and a Happiness Index) lead to more plausible responses to disasters. Applying a Differences-in-Differences approach and estimates from various panels of countries we find no noteworthy differences between the response of real GDP per capita and the responses of suggested alternative welfare measures to a natural disaster except for the Human Development Index.","PeriodicalId":53705,"journal":{"name":"Journal of Economic and Social Measurement","volume":"41 1","pages":"265-287"},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/JEM-160429","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70046164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Estimates of health care expenses for the U.S. population are critical to policymakers and others concerned with access to medical care and the cost and sources of payment for that care. Medical care expenses, however, are highly concentrated among a relatively small proportion of individuals in the community population. Using information from the Household Component of the Medical Expenditure Panel Survey (MEPS-HC), this study provides detailed estimates of the concentration and persistence in the level of health care expenditures in the United States. Attention is given to identifying the characteristics of individuals with the highest levels of medical expenditures, in addition to those factors that are associated with low medical expense profiles. Analyses are included to discern the most salient factors that serve to predict the likelihood of experiencing high levels of medical expenditures in a subsequent year, in addition to the factors operational in predictions of experiencing low levels of medical expenditures in a subsequent year.
{"title":"The concentration of health care expenditures in the U.S. and predictions of future spending","authors":"S. Cohen","doi":"10.3233/JEM-160427","DOIUrl":"https://doi.org/10.3233/JEM-160427","url":null,"abstract":"Estimates of health care expenses for the U.S. population are critical to policymakers and others concerned with access to medical care and the cost and sources of payment for that care. Medical care expenses, however, are highly concentrated among a relatively small proportion of individuals in the community population. Using information from the Household Component of the Medical Expenditure Panel Survey (MEPS-HC), this study provides detailed estimates of the concentration and persistence in the level of health care expenditures in the United States. Attention is given to identifying the characteristics of individuals with the highest levels of medical expenditures, in addition to those factors that are associated with low medical expense profiles. Analyses are included to discern the most salient factors that serve to predict the likelihood of experiencing high levels of medical expenditures in a subsequent year, in addition to the factors operational in predictions of experiencing low levels of medical expenditures in a subsequent year.","PeriodicalId":53705,"journal":{"name":"Journal of Economic and Social Measurement","volume":"41 1","pages":"167-189"},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/JEM-160427","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70045826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Alberto Felettigh, Claire Giordano, G. Oddo, Valentina Romano
This paper provides a new set of monthly price-competitiveness indicators for 62 countries, which are to be adopted by the Bank of Italy as its new official indicators. We employ updated trade weights that take into account the highly relevant competitive pressures of local producers in all outlet markets while guaranteeing a vast geographical coverage in international standards. We also assess price competitiveness with respective to different sub-groups of trading partners, namely euro-area vs. non euro-area countries. Focusing on the four largest economies in the euro area, in the period 1999-2014 Germany and France's price competitiveness is found to have improved; it was roughly stable in Italy whereas it deteriorated in Spain.
{"title":"New indicators to assess price-competitiveness developments in the four largest euro-area countries and in their main trading partners","authors":"Alberto Felettigh, Claire Giordano, G. Oddo, Valentina Romano","doi":"10.3233/JEM-160432","DOIUrl":"https://doi.org/10.3233/JEM-160432","url":null,"abstract":"This paper provides a new set of monthly price-competitiveness indicators for 62 countries, which are to be adopted by the Bank of Italy as its new official indicators. We employ updated trade weights that take into account the highly relevant competitive pressures of local producers in all outlet markets while guaranteeing a vast geographical coverage in international standards. We also assess price competitiveness with respective to different sub-groups of trading partners, namely euro-area vs. non euro-area countries. Focusing on the four largest economies in the euro area, in the period 1999-2014 Germany and France's price competitiveness is found to have improved; it was roughly stable in Italy whereas it deteriorated in Spain.","PeriodicalId":53705,"journal":{"name":"Journal of Economic and Social Measurement","volume":"41 1","pages":"203-235"},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/JEM-160432","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70046481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper we have looked into the problems of measuring visible underemployment. Some measures to gauge the intensity of visible underemployment have been suggested and their properties studied. Measures based on given time-norms for work are also suggested. The results are illustrated through examples.
{"title":"Measuring visible underemployment","authors":"S. S. Roy, Sourav Chakrabortty","doi":"10.3233/JEM-160426","DOIUrl":"https://doi.org/10.3233/JEM-160426","url":null,"abstract":"In this paper we have looked into the problems of measuring visible underemployment. Some measures to gauge the intensity of visible underemployment have been suggested and their properties studied. Measures based on given time-norms for work are also suggested. The results are illustrated through examples.","PeriodicalId":53705,"journal":{"name":"Journal of Economic and Social Measurement","volume":"41 1","pages":"85-101"},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/JEM-160426","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70045629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In addition to existing model specification tests based on over identifying information from pre-program periods, this paper conducts a new nonparametric test based on the concept of unconditional bounding. This test has the advantage that it does not require pre-program information on participants. The nonparametric bounding test yields similar information about model misspecification to the standard test based on over identifying information. As an illustration, we apply the test to administrative records from the Jobs First experiment in Connecticut. Records on other welfare recipients are used to provide quasi-experimental panel regression estimates of the impact of time limiting benefits on labor market earnings.
{"title":"Model specification tests and the estimation of treatment effects: An application with random and non-random administrative records","authors":"Tao Chen, K. Couch","doi":"10.3233/JEM-160423","DOIUrl":"https://doi.org/10.3233/JEM-160423","url":null,"abstract":"In addition to existing model specification tests based on over identifying information from pre-program periods, this paper conducts a new nonparametric test based on the concept of unconditional bounding. This test has the advantage that it does not require pre-program information on participants. The nonparametric bounding test yields similar information about model misspecification to the standard test based on over identifying information. As an illustration, we apply the test to administrative records from the Jobs First experiment in Connecticut. Records on other welfare recipients are used to provide quasi-experimental panel regression estimates of the impact of time limiting benefits on labor market earnings.","PeriodicalId":53705,"journal":{"name":"Journal of Economic and Social Measurement","volume":"41 1","pages":"1-16"},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/JEM-160423","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70045705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In a 1995 the National Research Council released a report that critically examined poverty measurement in the United States and recommended the development of a measure of medical expenditure related economic risk; a 2012 report continued support for such a measure. This study uses the Medical Expenditure Panel Survey to examine two alternative strategies for classifying individual-level risk for purposes of developing a loss modeling-based measure of medical care economic risk (MCER). Examining the use of self-perceived health and a DxCG risk score to classify individuals into five levels of risk, the study finds substantial differences in cell-level classification and attributed expenditure risk based on these two strategies. It is suggested that future work in this field move forward with the use of a risk score classification strategy to operationalize the MCER measure.
{"title":"Examining risk classification strategies for the development of a measure of medical care economic risk in the United States","authors":"S. Meier","doi":"10.3233/JEM-160430","DOIUrl":"https://doi.org/10.3233/JEM-160430","url":null,"abstract":"In a 1995 the National Research Council released a report that critically examined poverty measurement in the United States and recommended the development of a measure of medical expenditure related economic risk; a 2012 report continued support for such a measure. This study uses the Medical Expenditure Panel Survey to examine two alternative strategies for classifying individual-level risk for purposes of developing a loss modeling-based measure of medical care economic risk (MCER). Examining the use of self-perceived health and a DxCG risk score to classify individuals into five levels of risk, the study finds substantial differences in cell-level classification and attributed expenditure risk based on these two strategies. It is suggested that future work in this field move forward with the use of a risk score classification strategy to operationalize the MCER measure.","PeriodicalId":53705,"journal":{"name":"Journal of Economic and Social Measurement","volume":"41 1","pages":"289-305"},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/JEM-160430","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70046278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper considers regression techniques for grouped data. In particular, it is shown how regression statistics obtained from individual level data can be replicated by means of grouped data. Three common regression approaches are considered: ordinary least squares, instrumental variables and nonlinear least squares regression. Also provided is code to implement the grouped-data techniques in the econometric software package Stata. An empirical example illustrates that the grouped-data formulas indeed replicate the statistics obtained from the individual level data. It is also argued why grouped data are important for empirical research.
{"title":"Computing regression statistics from grouped data","authors":"Jörg Schwiebert","doi":"10.3233/JEM-150416","DOIUrl":"https://doi.org/10.3233/JEM-150416","url":null,"abstract":"This paper considers regression techniques for grouped data. In particular, it is shown how regression statistics obtained from individual level data can be replicated by means of grouped data. Three common regression approaches are considered: ordinary least squares, instrumental variables and nonlinear least squares regression. Also provided is code to implement the grouped-data techniques in the econometric software package Stata. An empirical example illustrates that the grouped-data formulas indeed replicate the statistics obtained from the individual level data. It is also argued why grouped data are important for empirical research.","PeriodicalId":53705,"journal":{"name":"Journal of Economic and Social Measurement","volume":"39 1","pages":"283-303"},"PeriodicalIF":0.0,"publicationDate":"2015-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/JEM-150416","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70045236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Consistent with the residence concept of BPM6 and the SNA2008, the U.S. current account reflects international transactions within multinational enterprises (MNEs), including international transactions conducted with special purpose entities (SPEs). To better understand the role of SPEs in economic accounting statistics, international guidelines on foreign direct investment (FDI) positions and transactions recommend that compilers distinguish resident SPEs for inbound FDI and encourage compilers to offer supplemental measures on non-resident SPEs for outbound FDI. While U.S. economic accounting statistics are not significantly affected by resident SPEs, recent empirical evidence calls into question the extent to which U.S. statistics may be affected by non-resident SPEs (Lipsey 2009, 2010). In this paper, I explore formulary apportionment as an accounting treatment for transactions related to outbound FDI in the U.S. current account in order to better understand the effects of non-resident SPEs on U.S. economic accounting statistics. The empirical results reveal that formulary apportionment significantly reduces total U.S. exports of services and total U.S. imports of services but the combined effect on U.S. net exports is negligible with no noticeable effect on U.S. gross domestic product (GDP). Likewise, formulary apportionment significantly reduces total U.S. income receipts, which reduces U.S. gross national product by 1.1 percent. The results imply that transactions attributable to non-resident SPEs do not affect U.S. net exports or U.S. GDP. Likewise, nonresident SPEs appear to play a larger role in income-based measures of production than in expenditure-based measures of production.
{"title":"Formulary Measures for the U.S. Current Account: Accounting for Transactions Attributable to Special Purpose Entities of Multinational Enterprises *","authors":"Dylan G. Rassier","doi":"10.3233/JEM-150400","DOIUrl":"https://doi.org/10.3233/JEM-150400","url":null,"abstract":"Consistent with the residence concept of BPM6 and the SNA2008, the U.S. current account reflects international transactions within multinational enterprises (MNEs), including international transactions conducted with special purpose entities (SPEs). To better understand the role of SPEs in economic accounting statistics, international guidelines on foreign direct investment (FDI) positions and transactions recommend that compilers distinguish resident SPEs for inbound FDI and encourage compilers to offer supplemental measures on non-resident SPEs for outbound FDI. While U.S. economic accounting statistics are not significantly affected by resident SPEs, recent empirical evidence calls into question the extent to which U.S. statistics may be affected by non-resident SPEs (Lipsey 2009, 2010). In this paper, I explore formulary apportionment as an accounting treatment for transactions related to outbound FDI in the U.S. current account in order to better understand the effects of non-resident SPEs on U.S. economic accounting statistics. The empirical results reveal that formulary apportionment significantly reduces total U.S. exports of services and total U.S. imports of services but the combined effect on U.S. net exports is negligible with no noticeable effect on U.S. gross domestic product (GDP). Likewise, formulary apportionment significantly reduces total U.S. income receipts, which reduces U.S. gross national product by 1.1 percent. The results imply that transactions attributable to non-resident SPEs do not affect U.S. net exports or U.S. GDP. Likewise, nonresident SPEs appear to play a larger role in income-based measures of production than in expenditure-based measures of production.","PeriodicalId":53705,"journal":{"name":"Journal of Economic and Social Measurement","volume":"20 1","pages":"257-281"},"PeriodicalIF":0.0,"publicationDate":"2015-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/JEM-150400","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70043685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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