CREAT: Census Research Exploration and Analysis Tool

Public Use Microdata: Disclosure And Usefulness

September 1988

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The 10 most similar working papers to the working paper 'Public Use Microdata: Disclosure And Usefulness' are listed below in order of similarity.

• Working Paper

  
  
  The Longitudinal Research Database (LRD): Status And Research Possibilities
  

  July 1988

  Authors: Robert H Mcguckin, George A Pascoe

  Working Paper Number:

  CES-88-02

  This paper discusses the development and use of the Longitudinal Research Data available at the Center for Economic Studies of the Bureau of the Census in terms of what has been accomplished thus far, what projects are currently in progress, and what plans are in place for the near future. The major achievement to date is the construction of the database itself, which contains data for manufacturing establishments collected by the Census in 1963, 1967, 1972, 1977 and 1982, and the Annual Survey of Manufactures for non-Census years from 1973 to 1985. These data now reside in the Center's computer in a consistent format across all years. In addition, a large software development task that greatly simplifies the task of selecting subsets of the database for specific research projects is well underway. Finally, a number of powerful microcomputers have been purchased for use by researchers for their statistical analysis. Current efforts underway at the Center include research on such policy-relevant issues as mergers and their impact on profits and production, high technology trade, import competition, plant level productivity, entry and exit, and productivity differences between large and small firms. Due to the confidentiality requirements of the Census data, most of their research is performed by Center staff and Special Sworn Employees. Under certain circumstances, the Center accepts user-written programs from outside researchers. These routines are executed by Center staff, and the resultant output is reviewed thoroughly for disclosure problems. The Center is also an active member of a task force working on methods on release "masked" or "cloned" microdata in public-use files that will protect the confidentiality of the data while at the same time provide a research tool for outside users. The Center research program contributes directly to future research possibilities. The current batch of research projects is adding insight into the nature of the LRD database. This information is continually being incorporated into the Center's software system, thus facilitating yet more research activity. Moreover, since a good portion of the research involves linking the Longitudinal Research Data to other data files, such as the NSF/Census R&D data, the scope of the databases is continually being expanded. Furthermore, the Center is exploring the possibility of linking the demographic data collected by the Census Bureau to the LRD database.
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• Working Paper

  
  
  Price Dispersion In U.S. Manufacturing: Implications For The Aggregation Of Products And Firms
  

  March 1992

  Authors: Thomas A Abbott Iii

  Working Paper Number:

  CES-92-03

  This paper addresses the question of whether products in the U.S. Manufacturing sector sell at a single (common) price, or whether prices vary across producers. Price dispersion is interesting for at least two reasons. First, if output prices vary across producers, standard methods of using industry price deflators lead to errors in measuring real output at the industry, firm, and establishment level which may bias estimates of the production function and productivity growth. Second, price dispersion suggests product heterogeneity which, if consumers do not have identical preferences, could lead to market segmentation and price in excess of marginal cost, thus making the current (competitive) characterization of the Manufacturing sector inappropriate and invalidating many empirical studies. In the course of examining these issues, the paper develops a robust measure of price dispersion as well as new quantitative methods for testing whether observed price differences are the result of differences in product quality. Our results indicate that price dispersion is widespread throughout manufacturing and that for at least one industry, Hydraulic Cement, it is not the result of differences in product quality.
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• Working Paper

  
  
  Multiple Classification Systems For Economic Data: Can A Thousand Flowers Bloom? And Should They?
  

  December 1991

  Authors: Robert H Mcguckin

  Working Paper Number:

  CES-91-08

  The principle that the statistical system should provide flexibility-- possibilities for generating multiple groupings of data to satisfy multiple objectives--if it is to satisfy users is universally accepted. Yet in practice, this goal has not been achieved. This paper discusses the feasibility of providing flexibility in the statistical system to accommodate multiple uses of the industrial data now primarily examined within the Standard Industrial Classification (SIC) system. In one sense, the question of feasibility is almost trivial. With today's computer technology, vast amounts of data can be manipulated and stored at very low cost. Reconfigurations of the basic data are very inexpensive compared to the cost of collecting the data. Flexibility in the statistical system implies more than the technical ability to regroup data. It requires that the basic data are sufficiently detailed to support user needs and are processed and maintained in a fashion that makes the use of a variety of aggregation rules possible. For this to happen, statistical agencies must recognize the need for high quality microdata and build this into their planning processes. Agencies need to view their missions from a multiple use perspective and move away from use of a primary reporting and collection vehicle. Although the categories used to report data must be flexible, practical considerations dictate that data collection proceed within a fixed classification system. It is simply too expensive for both respondents and statistical agencies to process survey responses in the absence of standardized forms, data entry programs, etc. I argue for a basic classification centered on commodities--products, services, raw materials and labor inputs--as the focus of data collection. The idea is to make the principle variables of interest--the commodities--the vehicle for the collection and processing of the data. For completeness, the basic classification should include labor usage through some form of occupational classification. In most economic surveys at the Census Bureau, the reporting unit and the classified unit have been the establishment. But there is no need for this to be so. The basic principle to be followed in data collection is that the data should be collected in the most efficient way--efficiency being defined jointly in terms of statistical agency collection costs and respondent burdens.
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• Working Paper

  
  
  Synthetic Data and Confidentiality Protection
  

  September 2003

  Authors: Julia I. Lane, John M. Abowd

  Working Paper Number:

  tp-2003-10

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• Working Paper

  
  
  Simultaneous Edit-Imputation for Continuous Microdata
  

  December 2015

  Authors: Jerome P. Reiter, Hang J. Kim, Lawrence H. Cox, Alan F. Karr, Quanli Wang

  Working Paper Number:

  CES-15-44

  Many statistical organizations collect data that are expected to satisfy linear constraints; as examples, component variables should sum to total variables, and ratios of pairs of variables should be bounded by expert-specified constants. When reported data violate constraints, organizations identify and replace values potentially in error in a process known as edit-imputation. To date, most approaches separate the error localization and imputation steps, typically using optimization methods to identify the variables to change followed by hot deck imputation. We present an approach that fully integrates editing and imputation for continuous microdata under linear constraints. Our approach relies on a Bayesian hierarchical model that includes (i) a flexible joint probability model for the underlying true values of the data with support only on the set of values that satisfy all editing constraints, (ii) a model for latent indicators of the variables that are in error, and (iii) a model for the reported responses for variables in error. We illustrate the potential advantages of the Bayesian editing approach over existing approaches using simulation studies. We apply the model to edit faulty data from the 2007 U.S. Census of Manufactures. Supplementary materials for this article are available online.
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• Working Paper

  
  
  New Approaches to Confidentiality Protection Synthetic Data, Remote Access and Research Data Centers
  

  June 2004

  Authors: Julia I. Lane, John M. Abowd

  Working Paper Number:

  tp-2004-03

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• Working Paper

  
  
  An Economist's Primer on Survey Samples
  

  September 2000

  Authors: William J Carrington, John L Eltinge, Kristin McCue

  Working Paper Number:

  CES-00-15

  Survey data underlie most empirical work in economics, yet economists typically have little familiarity with survey sample design and its effects on inference. This paper describes how sample designs depart from the simple random sampling model implicit in most econometrics textbooks, points out where the effects of this departure are likely to be greatest, and describes the relationship between design-based estimators developed by survey statisticians and related econometric methods for regression. Its intent is to provide empirical economists with enough background in survey methods to make informed use of design-based estimators. It emphasizes surveys of households (the source of most public-use files), but also considers how surveys of businesses differ. Examples from the National Longitudinal Survey of Youth of 1979 and the Current Population Survey illustrate practical aspects of design-based estimation.
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• Working Paper

  
  
  Plant-Level Productivity and Imputation of Missing Data in the Census of Manufactures
  

  January 2011

  Authors: T. Kirk White, Amil Petrin, Jerome P. Reiter

  Working Paper Number:

  CES-11-02

  In the U.S. Census of Manufactures, the Census Bureau imputes missing values using a combination of mean imputation, ratio imputation, and conditional mean imputation. It is wellknown that imputations based on these methods can result in underestimation of variability and potential bias in multivariate inferences. We show that this appears to be the case for the existing imputations in the Census of Manufactures. We then present an alternative strategy for handling the missing data based on multiple imputation. Specifically, we impute missing values via sequences of classification and regression trees, which offer a computationally straightforward and flexible approach for semi-automatic, large-scale multiple imputation. We also present an approach to evaluating these imputations based on posterior predictive checks. We use the multiple imputations, and the imputations currently employed by the Census Bureau, to estimate production function parameters and productivity dispersions. The results suggest that the two approaches provide quite different answers about productivity.
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• Working Paper

  
  
  Estimating the Distribution of Plant-Level Manufacturing Energy Efficiency with Stochastic Frontier Regression
  

  March 2007

  Authors: Gale Boyd

  Working Paper Number:

  CES-07-07

  A feature commonly used to distinguish between parametric/statistical models and engineering models is that engineering models explicitly represent best practice technologies while the parametric/statistical models are typically based on average practice. Measures of energy intensity based on average practice are less useful in the corporate management of energy or for public policy goal setting. In the context of company or plant level energy management, it is more useful to have a measure of energy intensity capable of representing where a company or plant lies within a distribution of performance. In other words, is the performance close (or far) from the industry best practice? This paper presents a parametric/statistical approach that can be used to measure best practice, thereby providing a measure of the difference, or 'efficiency gap' at a plant, company or overall industry level. The approach requires plant level data and applies a stochastic frontier regression analysis to energy use. Stochastic frontier regression analysis separates the energy intensity into three components, systematic effects, inefficiency, and statistical (random) error. The stochastic frontier can be viewed as a sub-vector input distance function. One advantage of this approach is that physical product mix can be included in the distance function, avoiding the problem of aggregating output to define a single energy/output ratio to measure energy intensity. The paper outlines the methods and gives an example of the analysis conducted for a non-public micro-dataset of wet corn refining plants.
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• Working Paper

  
  
  Analytic Use Of Economic Microdata; A Model For Researcher Access With Confidentiality Protection
  

  August 1992

  Authors: Robert H Mcguckin

  Working Paper Number:

  CES-92-08

  A primary responsibility of the Center for Economic Studies (CES) of the U.S. Bureau of the Census is to facilitate researcher access to confidential economic microdata files. Benefits from this program accrue not only to policy makers--there is a growing awareness of the importance of microdata for analyzing both the descriptive and welfare implications of regulatory and environmental changes--but also and importantly to the statistical agencies themselves. In fact, there is substantial recent literature arguing for the proposition that the largest single improvement that the U.S. statistical system could make is to improve its analytic capabilities. In this paper I briefly discuss these benefits to greater access for analytical work and ways to achieve them. Due to the nature of business data, public use databases and masking technologies are not available as vehicles for releasing useful microdata files. I conclude that a combination of outside and inside research programs, carefully coordinated and integrated is the best model for ensuring that statistical agencies reap the gains from analytic data users. For the United States, at least, this is fortuitous with respect to justifying access since any direct research with confidential data by outsiders must have a "statistical purpose". Until the advent of CES, it was virtually impossible for researchers to work with the economic microdata collected by the various economic censuses. While the CES program is quite large, as it now stands, researchers, or their representatives, must come to the Census Bureau in Washington, D.C. to access the data. The success of the program has led to increasing demands for data access in facilities outside of the Washington, D.C. area. Two options are considered: 1) Establish Census Bureau facilities in various universities or similar nonprofit research facilities and 2) Develop CES regional operations in existing Census Bureau regional offices.
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