EAGER: Collaborative Research: Examining Links Between Innovation and Economic Development Using Business R&D and Innovation Survey Microdata

University Of Illinois At Urbana-Champaign, Urbana IL

Investigators

Abstract

This Early-Concept Grant for Exploratory Research (EAGER) will support a project that will use Business R&D and Innovation Survey (BRDIS) microdata to test whether innovation results in job growth and whether particular types of innovation and industries generate more growth than others. The project will provide information and insights regarding the linkages between innovation and economic development by identifying the impact of specific types of innovative activities on industry-level growth in output and employment. Project results will provide information about the relative contribution of different types of technological, product, and process innovations to employment growth. Because the investigators will evaluate the role that industry characteristics play in converting innovation into economic outcomes, the project will provide framework for relating research on innovation to the goal of economic development. By distinguishing a variety of relationships among types of innovation, types of industries, and economic development, the project will help to adjust and expand innovation policies currently focused on high-technology sectors. The investigators will conduct statistical analyses of a range of distinctive valuables in the BRDIS database developed by the U.S. Bureau of the Census, including variables that deal with prototyping, product innovation, process innovation, and other innovation activities. They will conduct systematically test the many of the policy-relevant conclusions of existing case-study research, and they will extract industry-level innovation and employment indicators that can be used by other researchers. This EAGER-supported project responded to a Dear College Letter issued by the NSF Science of Science and Innovation Policy (SciSIP) Program designed to facilitate the use of STAR METRICS, NCSES, and other databases in order to evaluate relationships among science funding, employment, and outcome metrics.