Journal of Economic and Social Measurement - Volume 38, issue 4
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ISSN 0747-9662 (P)
ISSN 1875-8932 (E)
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.
Abstract: This work describes the process of building Micro.3, an integrated database containing firm level information for Italian enterprises. Micro.3 covers the period 1989–2004 and represents the development of a previous database, Micro.1 which covered a much narrower set of variables…and spanned only from 1989 to 1997. This works explains the motives that drove the development of Micro.3 and it details the decisions taken during the process of construction to ensure the continuity of the existing variables as they are retrieved from different sources. Finally, we presents some statistical evidence that enables the reader to assess the quality of Micro.3.
Keywords: Firm-level data, integrated database, Structural Business Statistics, unbalanced panel
Abstract: Individual attitudes and opinions may visibly impact upon an individual's decisions on how and when to use health care services and associated decisions with respect to medical expenditures. These health care preferences also serve as important inputs in helping to…predict health insurance coverage take-up decisions. This paper considers the degree of concordance over time in health care attitudes regarding the need and value of health insurance coverage based on national data from the Medical Expenditure Panel Survey. It demonstrates that individuals who consistently indicated they were healthy and did not need coverage were substantially less likely to have a medical expense in both years, relative to their counterparts who consistently disagreed with that classification. The paper also finds that adults under the age of 65 who consistently indicated that health insurance was not worth the cost were at nearly three times as likely to be continuously uninsured relative to those who consistently disagreed.
Keywords: Healthcare preferences, medical expenditures, health insurance, MEPS
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.