Expanding International Microdata Access: Linked German Survey and Administrative Data

该项目维护并扩大了德国就业研究所（IAB）和美国研究人员之间的国际数据共享。 其原则是为研究人员提供更方便的访问机密调查和行政数据，同时通过在安全的研究数据中心或数据飞地之间建立安全连接来维护数据安全，这些数据中心或数据飞地在数据和保密性保护方面具有可比标准。 PI已经在密歇根大学社会研究所的密歇根老龄人口统计中心建立了这样一个数据飞地，对这种方法进行了试点。 该奖项以试点为基础，支持密歇根飞地的持续运营以及扩展到美国研究数据中心网络的其他地点，包括康奈尔大学和加州大学伯克利分校的地点。数据共享安排允许合格的研究人员访问极其丰富的德国调查和管理数据。 这些数据可以相互关联，具有面板结构，涵盖了1975年至今德国（包括前东德德国）的所有企业和员工。 这些数据将用于比较美国和德国劳动力市场、宏观经济动态、国际贸易和产业组织的尖端统计分析。 这些数据对广泛的方法研究也很有用。 该项目还有助于研究生教育;获得德国数据的便利性意味着研究生可以以实用的方式练习高级数据分析技能。

This project maintains and expands international data sharing between the German Institute for Employment Research (IAB) and US researchers. The principle is to provide researchers with increased ease of access to confidential survey and administrative data while maintaining data security by establishing a secure connection between secure Research Data Centers or data enclaves which share comparable standards with regard to data and confidentiality protection. The PIs have piloted this approach with the establishment of such a data enclave at the Michigan Center for the Demography of Aging at the Institute for Social Research at the University of Michigan. This award builds on the pilot by supporting the continued operation of the Michigan enclave and the expansion to other locations in the US Research Data Center network, including locations at Cornell and the University of California, Berkeley.The data sharing arrangement allows qualified researchers access to extremely rich German survey and administrative data. The data can be linked, have a panel structure, and cover all firms and employees in Germany (including the former East Germany) from 1975 to the present. These data will be used in cutting-edge statistical analyses that compare US and German labor markets, macroeconomic dynamics, international trade, and industrial organization. The data are also useful for a wide range of methodological research. The project also contributes to graduate education; the ease of access to the German data means that graduate students can practice advanced data analysis skills in a practical way.