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Building a Knowledge Economy Index for Southern Metropolitan Areas Kristine Koutout Clemson University, Kristine.Renee@Gmail.Com Clemson University TigerPrints All Theses Theses 12-2008 Building a Knowledge Economy Index for Southern Metropolitan Areas Kristine Koutout Clemson University, [email protected] Follow this and additional works at: https://tigerprints.clemson.edu/all_theses Part of the Economics Commons Recommended Citation Koutout, Kristine, "Building a Knowledge Economy Index for Southern Metropolitan Areas" (2008). All Theses. 489. https://tigerprints.clemson.edu/all_theses/489 This Thesis is brought to you for free and open access by the Theses at TigerPrints. It has been accepted for inclusion in All Theses by an authorized administrator of TigerPrints. For more information, please contact [email protected]. BUILDING A KNOWLEDGE ECONOMY INDEX FOR SOUTHERN METROPOLITAN AREAS A Thesis Presented to the Graduate School of Clemson University In Partial Fulfillment of the Requirements for the Degree Master of Arts Economics By Kristine F. Koutout December 2008 Accepted by: Dr. Michael T. Maloney, Committee Chair Dr. Bruce Yandle Dr. Raymond Sauer ABSTRACT The purpose of this thesis is to determine if the methodology used to build the South Carolina Research Authority Knowledge Economy Index (KEI) for states can be replicated and applied to Southern Metropolitan Statistical Areas (MSAs). Data to imitate the KEI measures for workforce education and fast growth firms were available at the MSA-level; however, academic R&D was used as a proxy for industrial R&D in this index because data was not available for MSAs. An index for Southern MSAs was built based on the coefficients from the OLS results. Workforce education was the most important factor for increasing mean per capita income. The fast growth firms variable was not significant in the regression. Academic R&D had a negative coefficient, the opposite sign of industrial R&D in the KEI. The residual analysis and the high rankings of university cities revealed this type of MSA to be critical to the index. Further analysis was performed on the education variable to determine the source of the high coefficient in the OLS results. Finally, this index was compared to other relevant indices. The thesis offers research-based suggestions for improving MSA-based Knowledge Economy indices. Other research is also recommended that focuses on university cities and the relationship between MSAs and the states in which they are located. ii ACKNOWLEDGEMENTS I would like to express my appreciation to the South Carolina Research Authority for providing financial support for this research. In addition, I would like to thank Dr. Bruce Yandle for his invaluable guidance throughout the project. I would also like to express my gratitude to Dr. Michael Maloney, who was instrumental in the completion of this thesis. iii TABLE OF CONTENTS Page TITLE PAGE....................................................................................................................i ABSTRACT.....................................................................................................................ii ACKNOWLEDGEMENTS............................................................................................iii LIST OF TABLES...........................................................................................................v CHAPTER 1. INTRODUCTION AND BACKGROUND ............................................1 Introduction..................................................................................1 Background..................................................................................7 2. BUILDING A STATISTICAL MSA KNOWLEDGE ECONOMY INDEX .......................................................................................18 Construction of Variables ..........................................................18 Statistical Analyses ....................................................................21 Index Construction.....................................................................39 Comparative Rankings...............................................................42 Discussion..................................................................................47 3. CONCLUDING REMARKS Towards an Improved Index ......................................................51 Conclusion .................................................................................59 APPENDICES ...............................................................................................................61 BIBLIOGRAPHY..........................................................................................................77 iv LIST OF TABLES Table Page 1 Regression Results for Watkins’ Final Model....................................................6 2 Descriptive Statistics for All MSAs..................................................................22 3 Regression Results for All MSAs.....................................................................22 4 MSAs in Subset of SC, NC, GA, TN, and AL..................................................25 5 Descriptive Statistics for MSAs in Neighboring States....................................26 6 Regression Results for MSAs in Neighboring States .......................................27 7 South Carolina MSAs .......................................................................................29 8 Descriptive Statistics for SC MSAs..................................................................29 9 Residuals for All MSAs....................................................................................31 10 Outliers- Over Prediction..................................................................................31 11 Outliers- Under Prediction................................................................................32 12 Residuals for Neighboring MSAs.....................................................................33 13 Outliers- Over Prediction..................................................................................33 14 Outliers- Under Prediction................................................................................33 15 Regression Results of Education Pieces ...........................................................35 16 Regression Results with College Degrees ........................................................36 17 Regression Results with Graduate Degrees ......................................................37 18 Regression of All MSAs without Education Variable......................................38 19 Top 15 MSAs....................................................................................................40 20 Top 5 Neighboring MSAs.................................................................................41 v List of Tables (Continued) Table Page 21 SC MSAs ..........................................................................................................42 22 Regression on Milken’s Rankings- Large.........................................................43 23 Regression on Milken’s Rankings- Small.........................................................44 24 Regression on Florida’s Creativity Rank..........................................................45 25 Ranks Compared to the KEI .............................................................................46 26 Regression on the KEI ......................................................................................46 vi Chapter 1 INTRODUCTION AND BACKGROUND Introduction The transition to a Knowledge Economy is an important topic in research for a wide range of sciences. The effects of this evolution in the economy are comparable to the changes in society caused by the introduction of steam power at the beginning of the Industrial Revolution and the widespread usage of the assembly line in the 1920s. Every aspect of life has been affected by the accelerating rate of technological change, but it is the recognition of knowledge as not just a tool, but as a primary product, that has revolutionized the economy. Economists are interested in the Knowledge Economy for a number of reasons, one of which is the effects on individual welfare. Figure 1, from a presentation by Bruce Yandle (2008), plots U.S. state per capita income to the share of state population, 25 years and older, with a bachelor’s degree. This mapping illustrates the strength of the relationship between education and per capita GDP. 1 U.S. State Bachelor's Degrees & Per Capita GDP, 2006 45.0 40.0 35.0 s e e r 30.0 g . e d h t i 25.0 w 5 2 e v o 20.0 b a f o e r 15.0 a h S 10.0 5.0 0.0 0 10000 20000 30000 40000 50000 60000 70000 Per Capita GDP Policymakers are interested in improving per capita GDP, one measure of individual welfare. According to the figure, there are two ways to move right towards increased GDP. One, legislators can work towards increasing education on the y-axis, which will shift their state to the right. Two, policymakers could attempt to move to the right along the x-axis by supporting initiatives that reduce transaction costs thereby improving the use of knowledge in society. Regardless, the correlation between knowledge and welfare reveals a significant and positive relationship. The strength of the relationship enforces the dominant role of education in the Knowledge Economy. 2 The role of knowledge in the economy has long been recognized. John Stuart Mill proposes human capital as a factor that contributes to the growth of the economy in his (1848) The Principles of Political Economy. The production of wealth; the extraction of the instruments of human subsistence
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