The State New Economy Index

3. This is done by measuring the overall propensity to export (or patent or invest in R&D) of each 2-digit manufacturing sector, and multiplying the number of jobs in each 2-digit sector for each state by the 2-digit national propensity to export factor. These were summed to create an adjusted total number of jobs for each state. A ratio was calculated comparing the unadjusted to adjusted. If the ratio was higher than one it means that the state's industrial mix was slanted toward industries that tend to export less. If it was lower than one, the state had more jobs than the national average in industries that export more. The total value of exports was multiplied by the ratio for a final adjusted score.

4. This is not to say that all other areas of the states are not embracing New Economy development. For example, the area around Athens, Ga. has seen growth in agricultural biotech and the creation of a number of vaccine manufacturers.

5. See PPI's Metropolitan New Economy Index for more information on how metropolitan areas rank, www.neweconomyindex.org.

6. To control for the fact that IT workers are heavily employed in IT sectors, such as software, computer and office equipment, and computer related services, this indicator estimates the number of IT jobs in IT sectors and subtracts this number from the total number of workers in IT occupations in a state. This enables a more accurate picture of the extent to which traditional industries (e.g., other than software) employ IT professionals.

7. Managerial and professional jobs were calculated using 22 separate Occupational Employment Statistics codes from the Bureau of Labor Statistics.

8. Each state's residents were classified by education level. The percentage of residents with more than a high school degree but no college degree and those receiving an associate's degree were weighted with a multiplier of 0.5. The multiplier for the percentage of residents with a college degree was 1, and the multiplier for graduate degrees was 2. The weighted percentages were added to find each state's total score. In other words, a state where 10 percent of the residents had a high school degree and some college (earning a weighted score of 5), 20 percent with a bachelor's degree (a weighted score of 20), and 10 percent with a graduate degree (a weighted score of 20), would earn a total score of 45.

9. Stuart A. Rosenfeld and Robert D. Atkinson, "Engineering Regional Growth," Growth Policy in the Age of High Technology, ed. by Jurgen Schmandt and Robert Wilson (Boston: Unwin Hyman, 1990).

10. Scores were calculated by combining the state's score on the average educational level of the manufacturing workforce. But in order to control for the overall educational levels in the state, the measure also added the state's score on its relative score of the difference between the education levels of its entire workforce and manufacturing workforce. For example, if a state standard deviation was 0.76 in educational level of the workforce and 0.95 in manufacturing workforce, it would receive a score of 1.71 for this sub-variable.

11. Paul Osterman, "Revolutionizing Work," Blueprint Magazine (Washington, DC: Democratic Leadership Council, 2000), www.ndol.org.

13. Data on exports by state are available only for manufacturing. See Cletus Coughlin and Patricia Pollart, "Comparing Manufacturing Export Growth Across States: What Accounts for the Differences," Federal Reserve Bank of St. Louis Review, January/February 2001, pp. 15-16, www.stls.frb.org/docs/publica-tions/ review/01/0101cc.pdf.

14. To better measure the propensity of all companies to export, export scores are calculated by controlling for the overall industrial mix in each state. See endnote 3.

15. Andrew B. Bernard and J. Bradford Jensen, "Exporters, Jobs, and Wages in U.S. Manufacturing: 1976-1987," Brookings Papers in Microeconomics, (1995) pp. 67-119.

17. William J. Zeile, "U.S. Affiliates of Foreign Companies: Operations in 1999," Survey of Current Business, U.S. Department of Commerce, Bureau of Economic Analysis (2001).

18. The IPO measure is a weighted measure of the sum of the standard deviations for the number of IPOs as a share of GSP and the total value of IPOs as a share of GSP. IPO numbers are for 2000 and 2001.

21. The number of ".com" domains registered in a state will not be an exact measure of the number of businesses with Web sites for a number of reasons. For one thing, not all registered domains are in use. (Sometimes organizations register names they think they might use. And some domain names are held by speculators hoping to sell them.) Further, many ".com" domain names are registered by individuals for non-commercial purposes, to create personal Web pages, fan sites, and the like. And of the domains registered to businesses, not all of them are for commercial purposes, per se. (Some companies create rudimentary Web pages simply to make sure they're on the map, just as they might place an ad in the Yellow Pages. Others invest hundreds of thousands or millions of dollars building elaborate e-commerce systems in order to sell to markets around the world.) Nonetheless, these factors will be true across all states, and thus should cancel each other out.

23. Factors used in this indicator were students per multimedia computer; students per Internet connected computer; percentage of schools with Internet access through a T1 or cable modem, percentage of schools where at least 50 percent of teachers use the Internet in class; and the percentage of schools where at least 50 percent of teachers have school-based email addresses.

24. Cathy Ringstaff and Loretta Kelley, The Learning Return on Our Educational Technology Investment (San Francisco, Calif.: WestEd, 2002), www.WestEd.org/online_pubs/learning_return.pdf.

25. Education Week, "Technology Counts 2001: The New Divides" (May 2001), www.edweek.org.

26. To calculate the scores for this indicator, the standard deviation scores of each study's final score were combined and then divided by two.

27. Andrew Leigh and Robert D. Atkinson, Breaking Down Bureaucratic Barriers: The Next Phase of Digital Government (Washington, DC: Progressive Policy Institute, November 2001).

28. Tim Dunne, "Technology Usage in U.S. Manufacturing Industries: New Evidence From the Survey of Manufacturing Technology," Rand Journal of Economics, Vol. 25, no.3 Autumn, 1994, pp. 488-499.

29. A much better measure is the percentage of goods manufacturers buy and sell online. However, as with most Census Bureau data, it is collected but not released at the state level.

31. This indicator is a combined measure of high-speed lines (DSL, cable, and other methods) per household and establishment, and the percent of house-hold in ADSL range.

32. Kenan Patrick Jarboe and Robert D. Atkinson, The Case for Technology in the Knowledge Economy: R&D, Economic Growth, and the Role of Government (Washington, D.C.: Progressive Policy Institute, 1998), www.ppi.online.org.

34. This indicator includes the NAICs codes from the AEA definition, plus the following biomedical industries: NAICs codes 2833-2836; 3829, 3841-3843, 8071, 8099, 8731, 8733, and 8734. Altogether this includes computer and office equipment, consumer electronics, communications equipment, electronic components and accessories, semiconductors, industrial electronics, photonics, defense electronics, small arms, electro medical equipment, pharmaceuticals, optical instruments and lenses, navigational, medical, measuring and control instruments, medical equipment and supplies, scientific R&D services, medical and diagnostic laboratories, communications services and software and computer related services.

36. Scientists and engineers counted include only those who have attained a doctorate in their field.

37. To better measure the propensity of all companies to patent, patent scores are calculated by controlling for the overall industrial mix in each state. See endnote 3.

38. To better measure the propensity of all companies to invest in R&D, R&D scores are calculated by controlling for the overall industrial mix in each state. See endnote 3.

39. For more on the old and new models of economic development, see Richard Shatten and Paul Gottlieb, "Aha! Knowledge Economy," Innovation for Regional Advantage, vol. 2 (2000): pp 4-7.

40. One study pegged the figure at $48 billion per year. Kenneth Thomas, Competing for Capital: Europe and North America in a Global Era (Controversies in Public Policy), (Washington, DC: Georgetown University Press, 2000).

41. Jay Hancock, "Marriott used VA. as ruse to raise Md. Bid: Public records suggest Bethesda's firm's threat to leave was bluff," The Baltimore Sun, 27 March, 1999.

43. Robert D. Atkinson, "Modernizing Unemployment Insurance for the New Economy and the New Social Policy," (Washington, DC: Progressive Policy Institute, February 2002), www.ppionline.org.

44. Atkinson, "Building Skills for the New Economy: Regional Skills Alliances," (Washington, DC: Progressive Policy Institute, February 1998), www.ppionline.org.

46. Suzanne Teegarden and Barbara Baran, "The Promise of the Workforce Investment Act," Progressive Policy Institute, Washington, 2000. www.ppionline.org.

47. This section is based on a forthcoming report from Brian Bosworth, The FutureWorks Company, Cambridge, Mass.

48. John Comings, Andrew Sum, and Johan Uvin, "New Skills for a New Economy: Adult Education's Key Role in Sustaining Economic Growth and Expanding Opportunity," (Boston, Mass: MassInc, December 2000), www.massinc.org.

49. Robert Atkinson, "Making the New Economy Grow: An Action Agenda," (Washington, DC: Progressive Policy Institute, July 2000), www.ppionline.org.

51. Grant Black, Department of Economics, Georgia State University, "Small Firm Innovation in Metropolitan Areas: Does the Local Technological Infrastructure Matter," presented at the Fall 2001 APPAM conference.

52. Louis G. Tornatzky, Paul Waugaman, and Dennis Gray, "Innovation U.: New University Roles in a Knowledge Economy," Southern Growth Policies Board, 2002.

53. Robert D. Atkinson, Boosting Technological Innovation Through the Research and Experimentation Tax Credit (Washington, DC: Progressive Policy Institute, May 1999), www.ppionline.org.

56. Blueprint: Ideas for a New Century, "Stuck! America's Growing Mobility Crisis - and How To Get Out of It" (September/October 2001), www.ndol.org/blueprint.

58. For an overview of rural broadband strategies, see Tom Bonnett, "Starting a Telecommunications Plan in Your Community," Rural Research Report, Illinois Institute of Rural Affairs, Spring 2001, www.iira.org.

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