Doctoral Dissertation Research: A Statistical Analysis of the Location Factors of High-Tech Centers in the United States, 1950-1997: An Evaluation Using Quasi-Experimental Method

University Of Southern California, Los Angeles CA

Investigators

Abstract

What are the location factors that explain successful high-tech centers? Do previous studies of the high-tech industry shed light on this question? If not, how can we identify plausible factors and causes? These questions will be addressed in this dissertation research project through a statistical study of U.S. high-tech centers. High-tech industries, as a major job generator and as a major industrial innovator, play a key role explaining the postwar U.S. economic prosperity. There has been considerable discussion of how to possibly duplicate the success of high-tech centers such as Silicon Valley, Route 128, or Research Triangle Park. Research has been directed at questions on the pivotal location factors of the major high-tech centers, in an attempt to assist planners and policy makers who desire to develop similar centers of innovation. Previous research has focused on: (1) the role of federal support, (2)universities as providers of basic research and as suppliers of educated workers, (3)business conditions such as local industry size, patent, venture capital, and foreign direct investment, (4) quality of life, which includes climate, cost of living, crime, recreation, and education, and (5) combinations of factors are considered significant. Most recent studies seek to identify the factors that explain the robustness of established centers, but a serious gap in these discussions is that they focus on the performance of the group of high-tech centers by themselves. The plausibility of conclusions from such studies is limited because, to our knowledge, no control groups were used. The proposed research would fill this gap by first identifying suitable control groups and then systematically testing hypotheses on the explanation of high-tech center success. Until the growth and development of these centers is clearly understood, it will be difficult to fathom the process on which a successful high-tech centers growth policy can be built. Other methods such as shift-share, econometric analysis, and panel data analysis will also be required to fully test the hypotheses. This research project will necessarily employ statistical approaches to identify reliable location factors that explain successful high-tech centers. Specifically, a quasi-experimental control group method will be used. This involves the selection of a control group with similar initial conditions as the successful high-tech centers, and requires measuring the differences between the control group and high-tech centers. County-level U.S. census data from 1950 will be utilized in order to exclude most endogenous effects. To prevent a scale problem among variables in the selection process of properly paired comparisons, Mahalanobis distance will be adopted; this is a measure frequently used in anthropology, biostatistics, epidemiology, and other disciplines (Isserman 1999). Regression analysis will be utilized to compare and to identify significant causal factors. Competing hypothesis will be tested, and conclusions and policy implications will be suggested. The results of this study should provide a better understanding of the success of selected high-tech centers. The results can offer guidelines for high-tech center development, more so than the available high-tech centers literature. In so doing, the study will shed light on plausible high-tech location policies that many local and regional governments and planners are interested in.