EITM: Developing the Tools to Understand Human Performance: An Empirical Infrastructure to Foster Research Collaboration

Cornell Univ - State: Awds Made Prior May 2010, Ithaca NY

Investigators

Abstract

Understanding of the workplace from the perspective of both employers and employees is vital for understanding human performance. Understanding the workplace can only occur if micro data on employers and employees are integrated, linked longitudinally and made accessible to the research community. Developing the data infrastructure for integrated data is a monumental task as the traditional approach towards data development is to collect data on households and businesses separately. Fortunately, such data collected separately can be integrated via the rich administrative data sets that contain information on both employers and employees that are available in the U.S. federal statistical system. Developing an access system for such data is also a monumental task because the underlying data on businesses and households are protected by legal confidentiality restrictions. Within the federal statistical system, integrated micro data can be created and the challenge is to make such data accessible to the user community for approved statistical purposes while protecting the confidentiality of the data. Existing access to such data is via an NSF/Census Research Data Center network. While this system has been very successful, there are a number of limitations so that, relative to the potential use of the micro data in the federal statistical system, the current use is very limited. This project outlines a multi-layered access structure that builds on recent data infrastructure developments and the access modalities as they currently exist. Key components of this multi-layered access structure are the development of inference-valid public use synthetic micro data, access to richer synthetic micro data at a virtual Research Data Center, and in turn limited access to the gold standard micro data in the Census/NSF Research Data Center network. The development of inference-valid synthetic data is a major undertaking at the frontier of statistical theory and applications. The development of the multi-layered access system is at the frontier of dealing with the confidentiality protection issues that must be confronted. The micro data on businesses and households (and especially the integrated data) are of fundamental importance for the social sciences and must be accessible to the research community but the confidentiality of these data must also be protected. This grant supports a prototype synthetic data system for one Census data product - the LEHD infrastructure files (individual, employer, job) to test the feasibility and usefulness of constructing synthetic data. Broader Impacts of the Proposed Activity The proposed activity has the potential for dramatically increasing access to micro data for the social science research community. This increased access will have broad impacts but even broader impacts arise for all scientific disciplines from the methodologies and protocols developed under this project. Rich integrated micro data on households and businesses are required to address a wide range of issues in the social sciences, health sciences, and environmental sciences. Developing such rich data, inference-valid synthetic data, and a multi-layered access system are issues confronting many different parts of the scientific community. Many social scientists from a wide range of disciplines will access the data system developed in this proposal