1. 1 The Location Decision of the Highly Educated: A Statewide Analysis Sarah Wakefield

2. 2 Motivation New Location Theory – Less focus on business, more on people Impact on State Economies – Observe positive impacts Develop Strategies to Attract Talented Individuals – Talented: Bachelor’s degree and higher

3. 3 My Contribution Analysis at a statewide level Inclusion of explanatory variables such as park visitors and number of libraries Analyze the effect of net migration with patent data

4. 4 Research Questions: What factors attract talent at the state level? Does the relevance of each factor change as education level increases? Is there an observable positive impact on state economies?

5. 5 Literature Review Kodrzycki (2001) – location decisions of the college educated – Positive net migration of recent college graduates into South Atlantic, Mountain, & Pacific regions – New England, East & West North Central, and East South Central lost the highest shares of college graduates

6. 6 Literature Review Florida (2002) – Key role of diversity Diversity attracts better than climate, recreational, and cultural amenities – Talent and high tech industries lead to higher regional incomes

7. 7 Literature Review Glaeser (2005) – Measure the effects of higher education on growth rates – An area with twice as many colleges in 1940 faced 4% faster growth rate after 1970 – Positive effect on wages for high and low skilled workers

8. 8 Data – By State Dependent Variables: – Net Migration Bachelor’s – Net Migration Master’s – Net Migration Doctorate U.S. Census Bureau 1995- 2000 – Number of Patents U.S. Patent and Trademark Office 2005 – Economic Growth Bureau of Economic Analysis 2004-2005 Independent Variables: – Per Capita Income Bureau of Economic Analysis 2000 – Violent Crime Rate Bureau of Justice Statistics 2000 – Climate U.S. Dept. of Agriculture 1941-1970 – Number of Libraries Per Capita National Center for Education Statistics 2000 – Unemployment Rate Bureau of Labor Statistics 2000 – Restaurants National Restaurant Association 2000 – Percent Foreign Speaking U.S. Census 2000 – Park Visitors/Acreage U.S. Census 1997

9. 9 Methodology Multiple Linear Regressions: – Dependent variable = net migration of bachelor’s degrees, master’s degrees, and doctorate degrees

10. 10 Methodology Simple Linear Regressions: – Run regression with net migration by master and doctorate degrees

11. 11 Overview Location choice of talent can positively impact a state Understanding which factors draw in talent can help states develop positive economic policies Regression results will indicate which factors are significant, and if talent has a positive impact on state economies

12. 12 Descriptive Statistics VariablesNminmaxmeanstd Libraries500.07820.6584.955565.231233 Economic Growth518.73.4235292.077555 Patents4835179891588.5832749.328 % Foreign Speaking512.539.312.047068.948304 Per Capita Income50210054148928334.784413.04 Unemployment Rate502.36.23.84400.90535 Violent Crime Rate5181.41507.9443.3294241.1401 January Sun5122248148.986246.6818 January Temperature51919536.8637926.0762 July RH5114.97956.1384717.19921 July Temperature5161.891.1574.847875.664157 Park Acreage5093289249.74491.7086 Net Migration Bachelors and Above50-3464219107010701.2433345.37 Net Migration Bachelors50-32331140588694825054.24 Net Migration Masters50-2179392992796.446583.165 Net Migration Doctorate/Professional50-115911183955.862294.873 Bachelors & Above/Population508.109E-050.003976-0.010750.008209 Bachelors/Population50-0.00020720.003268-0.009050.00672 Masters/Population500.00021430.000598-0.001310.001314 Doctorates/Population507.403E-050.00024-0.000540.000557

13. 13 Tests of Multicollinearity and Heteroskedasticity For all four regressions of net migration, variance inflation factor < 10, so multicollinearity is not an issue Heteroskedasticity Tests: – Cannot reject null – No presence of heteroskedasticity ModelChi-SquareP Value Bachelors & Above47.900.6357 Bachelors49.950.5549 Masters49.510.5332 Doctorate49.860.5910

14. 14 ANOVA and Adjusted R-Squared Results ANOVA results: – Overall, each model is significant Adjusted R 2 : – Each model explains about 58% of the variation in net migration Model F ValueP Value Bachelors & Above6.89<.0001 Bachelors6.25<.0001 Masters6.93<.0001 Doctorate7.95<.0001 Model Adj. R 2 Bachelors & Above0.5460 Bachelors0.5174 Masters0.5474 Doctorate0.5864

15. 15 Results from Bachelors and Above Regression ParameterStandard VariableLabelEstimateErrort ValueP Value Intercept -0.012020.00974-1.230.2245 JanSunJanuary Sun-3.8E-061.57E-05-0.240.8087 JanTempJanuary Temperature1.88E-052.06E-050.910.3683 JulyRHJuly Relative Humidity-5.9E-062.65E-05-0.220.8237 JulyTempJuly Temperature-1.61E-059.62E-05-0.170.8676 vcrViolent Crime Rate4.97E-062.96E-061.680.1006 flPercent Speaking Foreign Language4.41E-057.57E-050.580.5637 incPer Capita Income3.992368E-73.76E-082.90.0061 libLibraries per Capita-0.000228.13E-05-2.760.0088 perparkPark Visitors/Acreage-1.3E-064.52E-06-0.30.7679 URUnemployment Rate0.0002540.0007230.350.7275

16. Interpretation of Results: If the violent crime rate increased by 1 (per 100,000) net migration would increase by.497 (per 100,000) If per capita libraries increase by 1, net migration would decrease by 22 people (per 100,000) A $1000 increase in income increases net migration by 40 people (per 100,000)

17. 17 Results from Bachelors Regression VariableLabelParameterStandardt ValueP Value EstimateError Intercept -0.009370.00825-1.140.2631 JanSunJanuary Sun-5.66472E-070.00001328-0.040.9662 JanTempJanuary Temperature0.000016960.000017490.970.3381 JulyRHJuly Relative Humidity-6.03E-060.00002241-0.270.7894 JulyTempJuly Temperature-0.000018870.00008156-0.230.8182 vcrViolent Crime Rate4.32E-060.000002511.720.0928 flPercent Speaking Foreign Language0.000021260.000064170.330.7422 incPer Capita Income3.02E-071.16597E-072.590.0133 libLibraries per Capita-0.00019730.00006891-2.860.0067 perparkPark Visitors/Acreage-9.42927E-070.00000383-0.250.8068 URUnemployment Rate0.000242330.000612610.40.6946

18. Interpretation of Results: If violent crime rate increases by 1 (per 100,000), net migration will increase by.432 people (per 100,000) If per capita libraries increase by 1, net migration decreases by 19.73 people (per 100,000) A $1000 increase in income increases net migration by 30.2 people (per 100,000)

19. 19 Results from Masters Regression VariableLabelParameterStandardt ValueP Value EstimateError Intercept -0.002320.00146-1.580.1211 JanSunJanuary Sun-0.000003060.00000235-1.30.2009 JanTempJanuary Temperature-3.57395E-070.0000031-0.120.9087 JulyRHJuly Relative Humidity1.30E-070.000003970.030.974 JulyTempJuly Temperature0.000009090.000014440.630.5326 vcrViolent Crime Rate4.14E-074.43759E-070.930.3566 flPercent Speaking Foreign Language0.000016640.000011361.460.1511 incPer Capita Income7.61E-082.06477E-083.690.0007 libLibraries per Capita-0.000017910.0000122-1.470.1501 perparkPark Visitors/Acreage-2.50172E-076.78293E-07-0.370.7143 URUnemployment Rate-0.000023770.00010848-0.220.8277

20. Interpretation of Results: Only per capita income is significant: – A $1000 increase in income increases net migration of those with a master’s by 7.61 people (per 100,000)

21. 21 Results from Doctorate Regression VariableLabelParameterStandardt ValueP Value EstimateError Intercept -0.000332180.00056193-0.590.5578 JanSunJanuary Sun-1.94955E-079.04631E-07-0.220.8305 JanTempJanuary Temperature0.000002180.000001191.830.0746 JulyRHJuly Relative Humidity-3.45E-080.00000153-0.020.9821 JulyTempJuly Temperature-0.000006360.00000555-1.150.2588 vcrViolent Crime Rate2.41E-071.70648E-071.410.1651 flPercent Speaking Foreign Language0.00000620.000004371.420.1641 incPer Capita Income2.08E-087.94009E-092.620.0125 libLibraries per Capita-0.000009030.00000469-1.930.0615 perparkPark Visitors/Acreage-1.50386E-072.60839E-07-0.580.5676 URUnemployment Rate0.000035140.000041720.840.4047

22. Interpretation of Results: 10 degree increase in January temperature increases doctorate migration by 2.18 (per 100,000) Increase of 1 in per capita libraries decreases doctorate migration by.903 (per 100,000) $1000 increase in income increases migration of doctorate holders by 2.08 (per 100,000)

23. 23 Positive Effects of Migration on a State Tests of Heteroskedasticity - Results of chi-square tests show no presence of heteroskedasticity PatentsChi- Square P Value Bachelors & above 1.08.5840 Bachelors0.97.6146 Masters1.52.4670 Doctorate1.83.4007 Economic GrowthChi- Square P Value Bachelors & above 0.33.8498 Bachelors0.71.7000 Masters1.77.4133 Doctorate0.09.9541

24. 24 Regression Results - Patents ModelIntercept α R2R2 Bachelors and Above.00024638 (<.0001).01089 (.1407).0253 Bachelors.00024962 (<.0001).01135 (.2095).0127 Masters.00022218 (<.0001).11895 (.0130).1057 Doctorate.00023711 (<.0001).14584 (.2320).0096

25. Interpretation of Results: Only master’s degree holders have a significant effect on patents Increase of 10 master’s degree holders in 2000 increases patents in 2005 by 1.1895 11% of the variation in patents issued is explained by net migration of master’s degree holders

26. 26 Regression Results – Economic Growth ModelIntercept λ R2R2 Bachelors and Above 3.37953 (<.0001) 129.0867 (.0903).0390 Bachelors3.42122 (<.0001) 150.65378 (.1047).0342 Masters3.22745 (<.0001) 758.51091 (.136).0258 Doctorate3.18379 (<.0001) 2785.64766 (.026).0810

27. Interpretation of Results: Increase of 1 with bachelor’s degree and higher increases growth by.129% Increase of 1 with bachelor’s degree increases growth by.151% Master’s degree holders – no significant impact Increase of 1 with a doctorate increases growth by 2.79%

28. Conclusion Per capita income is positive and relevant in the attraction of talent at all education levels Violent crime rate – positive effect on bachelor’s degree holders – Metropolitan areas Negative effect of per capita libraries on migration of bachelor’s and doctorate holders – Educational resources not a strong priority (sample only includes ages 22-29)

29. Implications Preferences do vary based on education level Positive effects of net migration exist and are significant Policy Initiatives: – Restructure tax codes – Attract businesses to increase salary – Emphasize metropolitan areas – To attract doctorate holders, promote climate

30. 30 Bibliography Florida, Richard. “The Economic Geography of Talent.” Annals of the Association of American Geographers, 92(4), p. 743-755; (2002). Glaeser, Edward. “Smart Growth: Education, Skilled Workers, and the Growth of Cold-Weather Cities.” Harvard University, April 2005. Gee, Wilson. “The ‘Drag’ of Talent out of the South.” Social Forces, Vol. 15, No. 3, p. 343-346; (1937). Harden, Brian. “Brain-Gain Cities Attract Educated Youth.” The Washington Post, 2004. Kodrzycki, Yolanda. “Migration of Recent College Graduates: Evidence from the National Longitudinal Survey of Youth.” New England Economic Review; (2001). Schwartz, Aba. “Migration, Age, and Education.” The Journal of Political Economy, Vol. 84, Issue 4, Part 1, p. 710-720; (1976)