Abstract
This article describes the results of a pilot study that tested the feasibility of estimating quantitatively the regional and economic impacts of NSF-supported Engineering Research centers. For regional impacts, we combined estimates of the direct plus indirect and induced economic impacts of ERC expenditures generated from a regional input–output model with estimates of the additional impact on the state due to center-based start-up companies, licensing income from intellectual property produced by the center, the cost savings enjoyed by local firms that had hired center graduates, and advice and consulting to local firms by center faculty. For national economic impact, a suitably modified version of the regional approach was employed, supplemented by use of a consumer surplus model to estimate the net public benefits of newly commercialized technologies based in center research. As the project proceeded, it became clear that efforts to focus solely on economic impacts that could be quantified relatively easily would greatly underestimate the actual national economic impact of ERCs. The types of impacts included and the kinds of data collected from centers and their collaborating companies were therefore expanded in the later case studies. Results of the first three cases are described here; findings from the remaining two studies did not change our overall results or conclusions. The profile of regional and, especially, national economic impact estimates varied widely across the centers studied. Only some of these variations could be attributed to ERC characteristics; most were the result of variations in the amount and type of data that could be obtained from the centers involved and the companies they worked with. We concluded that even the most conscientious and costly data collection efforts would be unlikely to yield comparable data across centers because the accessibility of key data, especially proprietary data, will differ unpredictably from center to center. Further, focusing on narrowly-conceived, quantifiable economic data alone should be avoided in these kinds of impact studies. Doing so distorts the amount and characteristics of actual impacts, many of which—perhaps most of which—cannot feasibly be converted to monetary terms. Such a narrow focus will greatly underestimate the impact of ERC-like centers, masking the much broader and, based on our findings, larger and more significant impacts on society.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
Caltech’s Center for Neuromorphic Systems Engineering (CNSE), Virginia Tech’s Center for Power Electronics Systems; University of Michigan’s Center for Wireless Integrated MicroSystems (WIMS).
The Center for Computer Integrated Surgical Systems and Technology at Johns Hopkins and the Georgia Tech/Emory Center for the Engineering of Living Tissue.
See Georghiou and Roessner (2000) for a brief review of these studies. Several extension program impact studies have been conducted since that time, generally following the same approach.
The limitations of RIMS II are well-known and need not be reviewed here. Despite its limitations, a more precise tool for indicating indirect and induced economic effects of new expenditures by organizations has not been developed, and the model remains widely used.
See Mansfield (1996) for a discussion of how the consumer surplus model can be applied to assessment of innovation-related public programs such as the Advanced Technology Program, and Mansfield (1977) for the original paper illustrating the calculation of social and private returns to industrial innovation. We are aware of the limitations of this approach, as well as those of alternative approaches, but chose it because of its feasibility for this pilot study with its attendant resource and data access constraints. Notable among virtually all economic impact methods is their inability to estimate quantitatively the economic impacts of the research and education benefits of ERCs and similar university-based centers.
Recently Scherer and Harhoff (2000) studied the size distribution of financial returns from eight sets of data on inventions and innovations attributable to private sector firms and universities. They found that the distributions were all highly skewed, with the top 10% of sample members capturing from 48 to 93% of the total sample returns.
Space limitations prevent us from providing all details of the calculations involved. These and other details, such as the size, technical foci, and industry affiliations of the ERCs studied, are provided in our final report to the National Science Foundation, available upon request from the lead author of this article.
Telephone interview with Richard Zhang and Vlatko Vlatkovic, General Electric Global Research, 27 July 2007; telephone interview with Mike Briere, Vice President for R&D, International Rectifier, 27 July 2007.
Obviously we made no effort in this study to assess the performance or productivity of ERCs with respect to either their own specific objectives or NSF’s mandated program goals. Nonperforming ERCs are quickly identified at an early stage in their history and either terminated or reorganized so that, by the end of their period of NSF support, it can be assumed that all ERCs are performing at a high level and achieving their basic research, education, and knowledge transfer goals.
Future work should address, for example, questions such as: What types of data are needed to properly address the outcomes of ERC research and education activities? How can the benefits of developing a new workforce to lead innovation be captured? How can the effects that ERC graduates have on the firms that hired them be assessed more systematically? How might a wider range of ERC impacts, such as changes in firms’ R&D agendas, new partnerships, stronger industry-university collaborations, enhanced competitiveness, and new relationships between firms and their suppliers and customers, be documented?
References
Adams, J. D., Chiang, E. P., & Starkey, K. (2001). Industry-university cooperative research centers. Journal of Technology Transfer, 26(1–2), 73–86.
Audretsch, D., & Feldman, M. (1996). R&D spillovers and the geography of innovation and production. American Economic Review, 86(3), 630–640.
Caffrey, J., & Isaacs, H. (1971). Estimating the impact of a college or university on the local economy. Washington D.C.: American Council on Education (ACE).
Carr, R., & Roessner, D. (2002). Economic impact of Michigan’s state universities. Arlington, VA: SRI International.
Feller, I. (1990). Universities as engines of R&D-based economic growth: They think they can. Research Policy, 19, 335–348.
Feller, I. (2004). Virtuous and vicious cycles in the contributions of public research universities to state economic development objectives. Economic Development Quarterly, 18(2), 138–150.
Feller, I., & Anderson, G. (1994). A benefit-cost approach to the evaluation of state technology development programs. Economic Development Quarterly, 8(2), 127–140.
Georghiou, L., & Roessner, D. (2000). Evaluating technology programs: Tools and methods. Research Policy, 29, 657–678.
Griliches, Z. (1958). Research costs and social returns: Hybrid corn and related innovations. Journal of Political Economy, 66(5), 419–431.
Jaffe, A. (1998). The real effects of academic research. American Economic Review, 79(5), 957–970.
Jaffe, A. B., Trajtenberg, M., & Henderson, R. (1993). Geographic localization of knowledge spillovers as evidenced by patent citations. The Quarterly Journal of Economics, 108(3), 577–598.
Link, A., & Scott, J. (1998). Public accountability: Evaluating technology-based institutions. Boston, MA: Kluwer.
Mansfield, E. (1977). Social and private rates of return from industrial innovation. Quarterly Journal of Economics, 91(2), 221–240.
Mansfield, E. (1991). Academic research and industrial innovation. Research Policy, 20, 1–12.
Mansfield, E. (1996). Estimating social and private returns from innovations based on the advanced technology program: Problems and opportunities. Gaithersburg, MD: NIST.
Roessner, D., Franco, Q., & Mohapatra, S. (2004). Economic impact on Georgia of Georgia Tech’s Packaging Research Center. Arlington, VA: SRI International.
Scherer, F. M., & Harhoff, D. (2000). Technology policy for a world of skew-distributed outcomes. Research Policy, 29, 559–566.
Tassey, G. (2003). Methods for assessing the economic impacts of government R&D. Gaithersburg, MD: NIST.
Zucker, L. G., Darby, M. R. (2005). Socio-economic impact of nanoscale science: Initial results and NanoBank. Working Paper 11181, National Bureau of Economic Research, Inc.
Zucker, L. G., Darby, M. R., Furner, J., Liu, R. C., & Ma, H. (2007). Minerva unbound: Knowledge stocks, knowledge flows and new knowledge production. Research Policy, 36(6), 850–863.
Acknowledgments
We gratefully acknowledge the support of National Science Foundation contract D050513 for the work described in this article. Any conclusions, findings, or recommendations in this article are those of the authors and do not necessarily reflect those of the National Science Foundation or the U.S. government.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Roessner, D., Manrique, L. & Park, J. The economic impact of engineering research centers: preliminary results of a pilot study. J Technol Transf 35, 475–493 (2010). https://doi.org/10.1007/s10961-010-9163-x
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10961-010-9163-x