Some Impacts of Industry Clusters in Missouri

1. Some Impacts of Industry Clusters in Missouri Dr. Diane Primont Professor of Economics & Associate Director, CEBR email: dprimont@semo.edu April 11, 2008

2. Introduction Economists and Economic Developers often focus on causes of disparities in economic growth rates Why? Even small differences in growth rates can lead to ever larger disparities over time, due to compounding

3. Two Research Questions Are disparities in economic performance among counties increasing or diminishing? “Convergence” How is this performance effected by the presence and extent of industry clusters? “Industry Clusters”

4. Two Research Questions Industry cluster a group of businesses linked by common supply chains, labor needs, technologies, or customers

5. 1. Are Missouri counties converging? Statistical Analysis Conditioning the growth model on: Rurality Industry cluster specialization Graphical evidence

6. Statistical Analysis: Conditioning the growth model • Conditional Growth Model Growth in real per capita income 2000-2005 depends on • Real per capita income in 2000 (lnpcinc2000) • Index of relative rurality (lnirr) • Industry cluster specialization (specialization) 1 if county specializes in one or more industry clusters; 0 otherwise

7. Statistical Analysis: Conditioning the growth model grpcinc | Coef. Std. Err. t P>|t| ------------------------------------------------------------- lnpcinc2000 | -.2204 .0342 -6.34 0.000 lnirr | -.0437 .0150 -2.91 0.004 specialize | .0208 .0096 2.16 0.033 constant | 2.0510 .3149 6.51 0.000 -------------------------------------------------------------R-squared = 0.3005 Adj R-squared = 0.2816

8. Graphical evidence of convergence Pulaski Oregon Reynolds St. Louis County Cape Girardeau Shelby

9. Growth by Rurality of County Reynolds Carter Wayne Monroe Scotland Shelby

10. Conclusions • The conditional growth model • suggests that Missouri counties are converging • rural counties tend to grow more slowly – will fall further and further behind • counties that specialize in one or more industry clusters tend to grow more quickly

11. 2. Industry Clusters in Missouri Measuring specialization Location Quotient Specialization in Industries Industry cluster bubble charts Northeast and South Central Missouri Southeast Missouri and the Bootheel

12. Measuring Specialization • Location Quotient • Ratio of the proportion of a region’s employment in an industry to that of the nation as a whole • LQ = (EX/ET)/(NX/NT) • EX is region’s employment industry x • ET is region’s total employment • NXis national employment in industry x • NTis total national employment

13. Measuring Specialization • LQ = 1: the region’s activity in the industry cluster is similar to the nation as a whole. • LQ < 1: the region’s activity in the industry is unspecialized. • LQ > 1: the region’s activity in the industry exceeds that of the nation as a whole. • The greater LQ exceeds 1, the more specialized the region is in the industry cluster.

14. Measuring Specialization

15. Industry Cluster Bubble Chart Mature 2 Stars LQ in 2005 1 -10 10 Transforming Emerging 0 % Chg. in LQ 2001-2005 Hypothetical Data

16. Industry Cluster Bubble Chart Forest & Wood Products Biomedical/Biotechnical Education & Knowledge Creation Energy

17. Industry Cluster Bubble Chart Manufacturing Supercluster Mining Chemicals Glass & Ceramics Forest & Wood

18. Industry Cluster Bubble Chart Biomedical/ Biotechnical Forest & Wood Products Transportation and Logistics Energy Defense & Security Business & Financial

19. Industry Cluster Bubble Chart Agribusiness Transportation and Logistics Biomedical/Biotechnical Energy Manufacturing Supercluster

20. Conclusions • Achieving high economic growth is a challenge for any county or region, but particularly for rural counties • Regions with a greater number of “mature,”“star,” and “emerging” industry clusters tend to grow faster • May be useful to target these industries for further development