{"title":"生产率增长测量中产能利用率变化的校正:一种非参数方法","authors":"Wulong Gu, Weimin Wang","doi":"10.3233/JEM-130380","DOIUrl":null,"url":null,"abstract":"The multifactor productivity growth estimate published by statistical agencies should be corrected for the effect of the short run variations in capacity utilization for such estimate to be a measure of technological progress. But such correction is not normally made as the rate of capacity utilization is often not observed. This paper develops a nonparametric approach for adjusting multifactor productive growth measure for variation in capacity utilization over time. In the approach developed here, the capital utilization measure is derived from the economic theory of production and is estimated by comparing the ex-post return with the ex-ante expected return on capital. The approach offers a practical solution that can be used by statistical agencies to adjust for capacity utilization in their multifactor productivity growth measure. The nonparametric approach is implemented using the data for the manufacturing sector from the Canadian Productivity Program of Statistics Canada, and is found to correct for the bias from the variation in capacity utilization in that sector.","PeriodicalId":53705,"journal":{"name":"Journal of Economic and Social Measurement","volume":"38 1","pages":"347-369"},"PeriodicalIF":0.0000,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/JEM-130380","citationCount":"4","resultStr":"{\"title\":\"Correction for variations in capacity utilization in the measurement of productivity growth: A non-parametric approach\",\"authors\":\"Wulong Gu, Weimin Wang\",\"doi\":\"10.3233/JEM-130380\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The multifactor productivity growth estimate published by statistical agencies should be corrected for the effect of the short run variations in capacity utilization for such estimate to be a measure of technological progress. But such correction is not normally made as the rate of capacity utilization is often not observed. This paper develops a nonparametric approach for adjusting multifactor productive growth measure for variation in capacity utilization over time. In the approach developed here, the capital utilization measure is derived from the economic theory of production and is estimated by comparing the ex-post return with the ex-ante expected return on capital. The approach offers a practical solution that can be used by statistical agencies to adjust for capacity utilization in their multifactor productivity growth measure. The nonparametric approach is implemented using the data for the manufacturing sector from the Canadian Productivity Program of Statistics Canada, and is found to correct for the bias from the variation in capacity utilization in that sector.\",\"PeriodicalId\":53705,\"journal\":{\"name\":\"Journal of Economic and Social Measurement\",\"volume\":\"38 1\",\"pages\":\"347-369\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.3233/JEM-130380\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Economic and Social Measurement\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3233/JEM-130380\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Social Sciences\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Economic and Social Measurement","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3233/JEM-130380","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Social Sciences","Score":null,"Total":0}
Correction for variations in capacity utilization in the measurement of productivity growth: A non-parametric approach
The multifactor productivity growth estimate published by statistical agencies should be corrected for the effect of the short run variations in capacity utilization for such estimate to be a measure of technological progress. But such correction is not normally made as the rate of capacity utilization is often not observed. This paper develops a nonparametric approach for adjusting multifactor productive growth measure for variation in capacity utilization over time. In the approach developed here, the capital utilization measure is derived from the economic theory of production and is estimated by comparing the ex-post return with the ex-ante expected return on capital. The approach offers a practical solution that can be used by statistical agencies to adjust for capacity utilization in their multifactor productivity growth measure. The nonparametric approach is implemented using the data for the manufacturing sector from the Canadian Productivity Program of Statistics Canada, and is found to correct for the bias from the variation in capacity utilization in that sector.
期刊介绍:
The Journal of Economic and Social Measurement (JESM) is a quarterly journal that is concerned with the investigation of all aspects of production, distribution and use of economic and other societal statistical data, and with the use of computers in that context. JESM publishes articles that consider the statistical methodology of economic and social science measurements. It is concerned with the methods and problems of data distribution, including the design and implementation of data base systems and, more generally, computer software and hardware for distributing and accessing statistical data files. Its focus on computer software also includes the valuation of algorithms and their implementation, assessing the degree to which particular algorithms may yield more or less accurate computed results. It addresses the technical and even legal problems of the collection and use of data, legislation and administrative actions affecting government produced or distributed data files, and similar topics. The journal serves as a forum for the exchange of information and views between data producers and users. In addition, it considers the various uses to which statistical data may be put, particularly to the degree that these uses illustrate or affect the properties of the data. The data considered in JESM are usually economic or social, as mentioned, but this is not a requirement; the editorial policies of JESM do not place a priori restrictions upon the data that might be considered within individual articles. Furthermore, there are no limitations concerning the source of the data.