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We describe a unique environment in which undergraduate students fromvarious STEM and social science disciplines are trained in data provenance and reproducible methods, and then apply that knowledge to real, conditionally accepted manuscripts and associated replication packages. We describe in detail the recruitment, training, and regular activities. While the activity is not part of a regular curriculum, the skills and knowledge taught through explicit training of reproducible methods and principles, and reinforced through repeated application in a real-life workflow, contribute to the education of these undergraduate students, and prepare them for post-graduation jobs and further studies. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

This paper evaluates the use of commuting zones as a local labour market definition. We revisit the seminal paper by Tolbert and Sizer and demonstrate the sensitivity of definitions to two features of the methodology: a cluster dissimilarity cut-off, or the count of clusters, and uncertainty in the input data. We show how these features impact empirical estimates using a standard application of commuting zones and an example from related literature. We conclude with advice to researchers on how to demonstrate the robustness of empirical findings to uncertainty in the definition of commuting zones. [ABSTRACT FROM AUTHOR]

This section presents the authors' response to criticisms to their article The Sensitivity of Economic Statistics to Coding Errors in Personal Identifiers, published in the April 2005 issue of the Journal of Business & Economic Statistics. We appreciate the time and effort that the discussants have spent providing us with concise and useful comments and suggestions. To begin, we want to point out that the methods proposed in our article have already been implemented within the U.S. Census Bureau as part of the production system for the Quarterly Workforce Indicators. Since the methods were prototyped and analyzed with California data, a number of states have been processed in much the same way. Van der Klauw is also worried that our procedure potentially introduces new biases. His example is the differential impact of the procedure on wage earners with high and low earnings volatility, as well as the differences in data transmission methods used by firms. Several commentators discussed the earnings model that we used to augment the matching procedure with economic pattern data. The application of the methods proposed in our article is not unique to administrative records data. In fact, we used a commercial record-linking product that is widely used in business environments. Statistical methods that can take into account knowledge about matching errors, surveyed in the forthcoming Handbook of Econometrics chapter suggested by van der Klauw, will become increasingly important. One issue that multiple commentators have mentioned is access to the data. The data that we used for this article are not public use. In particular, our proposed correction methods require access to highly sensitive confidential information. We agree with all of the discussants that efforts to improve the quality of administrative record data for use as an input to general statistical analyses must go hand-in-hand with efforts to elaborate conventional analysis models.

In this article we describe the sensitivity of small-cell flow statistics to coding errors in the identity of the underlying entities. Specifically, we present results based on a comparison of the U.S. Census Bureau's Quarterly Workforce Indicators before and after correcting for such errors in Social Security Number-based identifiers in the underlying individual wage records. The correction used involves a novel application of existing statistical matching techniques. It is found that even a very conservative correction procedure has a sizable impact on the statistics. The average bias ranges from 25% up to 15% for flow statistics, and up to 5% for payroll aggregates. [ABSTRACT FROM AUTHOR]