1. Economic Growth IN THE UNITED STATES OF AMERICA A County-level Analysis

2. Objective To explore the factors driving differences in regional economic growth across the United States. To replicate the analysis in the OECD paper, “The Sources of Economic Growth in OECD Regions: A Parametric Analysis,” (December 2008) for the U.S. case.

3. Agenda 1.Theory 2.Data 3.Summary Statistics 4.Results 5.Findings/Conclusion 6.Future research/Recommendations 7.Questions

4. What theories explain economic growth? 1.Neo-Classical Theory 2.Endogenous Growth Theory 3.New Economic Geography (NEG)

5. Neo-Classical Theory Assumes Diminishing Returns And Exogenous Technology Key assumptions: Capital is subject to diminishing returns Perfect competition An exogenously determined constant rate reflects the progress made in technology 3 Key factors: Capital intensities Human capital Technology (not included in the model; exogenous)

6. Neo-Classical Theory Predicts Convergence Long-run growth is the result of continuous technological progress, which is determined exogenously Key implication: Conditional convergence Problems Limited empirical evidence of convergence Leaves technological progress out of the model

7. Endogenous Growth Theory Assumes Diminishing Returns and Endogenous Technology Key assumptions: Capital is subject to diminishing returns In many endogenous growth models the assumption of perfect competition is relaxed, and some degree of monopoly power is thought to exist. 3 Key factors: Physical capital Human capital Technology (included in the model: endogenous)

8. Endogenous Growth Theory: Internal factors are the main sources of economic growth Investing in human capital  the development of new forms of technology & efficient and effective means of production  economic growth Investment in human capital (education and training of the workforce) is an essential ingredient of growth The main implication: policies which embrace openness, competition, change and innovation will promote growth. Theory emphasizes that private investment in R&D is the central source of technical progress No convergence is predicted.

9. Economic geography: the location of factors of production in space Key Implication Despite early similarity regions can become quite different! Key factors causing agglomeration or dispersion 1.Economies of scale 2.Transportation costs 3.Location of demand 4.Population New Economic Geography: Why is manufacturing concentrated in a few regions?

10. New Economic Geography predicts that the right mix of key factors causes growth How does differentiation occur? General NEG model answers One region slightly larger + transportation costs  + IRS + larger initial production = more people & production spatially close together This will allow the larger initial region to grow while the smaller initial region does not - or does so to a lesser degree and at a slower rate.

11. How does NEG differ from Neo-Classical and Endogenous Growth Theories? NEG takes scale into account NEG models propose that external increasing returns to scale incentivize agglomeration Agglomeration captures, via scale effects, how small initial differences cause large growth differentials over time

12. We obtained data on 3,079 counties between 1998-2007 VariableSourceYear(s) Annualized per capita personal income growth Bureau of Economic Analysis1998-2007 Log of income in the initial yearBureau of Economic Analysis1998 Physical capital/infrastructureESRI Data and Maps 9.3 Media Kit 2008 Education ratesU.S. Census2000 Innovation IndexEconomic Development Administration 2008 Employment rateBureau of Economic Analysis1998-2007 Employment specializationCensus of Employment and Wages 1998-2007 Accessibility to Markets/Distance to Markets ESRI Data and Maps 9.3 Media Kit Bureau of Economic Analysis 2008 1998

13. Per Capita Personal Income Ranges from $8,579 in Loup County, NE to $132,728 in Teton County, WY Used to create three variables: Dependent variable: annualized per capita personal income growth 1/10 * ln(income in 2007) – ln(income in 1998) Highest: 7% in Sublette, WY Lowest: -3% in Crowley, CO Mean: 1% Independent variable: log of income in the initial year, 1998 Highest: $76,450 in New York, NY Lowest: $7,756 in Loup, NE Independent variable: per capita personal income in nearby counties, weighted by distance and other spatial measures

17. Infrastructure A measure of Physical Capital. Mileage of major roads by county Airports by county

18. Major Road Mileage by County

19. Number of airports by County

20. Education Rates Source: 2000 Census Percent of population with less than high school degree Highest: 62.5% in Starr, TX Lowest: 4.4% in Douglas, CO Median: 21.6% Percent of population with a high school diploma Highest: 53.5% in Carroll, OH Lowest: 12.4% in Arlington, VA Median: 34.7% Percent of population with more than a high school degree Highest: 82.1% in Los Alamos, NM Lowest: 17.2% in McDowell, WV Median: 41.4% These three variables add up to 1 (Capture above info in bar graph)

21. Innovation Index [COMING SOON]

22. Employment Rate Source: 2000 Census (for cross-section) Youth employment rate: population aged 16 – 20 that is working divided by total population 16 – 20 Highest: 100% in Loving, TX Lowest: 8.78% in Shannon, SD Median: 46.2% Working age employment rate: population aged 21 – 65 that is working divided by total population 21 – 65 Highest: 88.4% in Stanley, SD Lowest: 35.9% in McDowell, WV Median: 73% Total employment rate Highest: 86.7% in Stanley, SD Lowest: 33.6% in McDowell, WV Median: 69.9% (NEED BAR GRAPH!)

23. Employment Specialization What is it? – Measure of industrial concentration of a region (county) What is it meant to capture? – Captures notion of agglomeration – What is agglomeration? The close spatial concentration of industry A determinant of economic growth in NEG growth theory – How is it modeled? Specialization indices Herfindahl Index Krugman Index

24. Employment Specialization Herfindahl Index (HI) Definition: – N Σ i=1 s 2 Features: – Ranges from 0 to 1.0 – 0 = industrial diversity (lots of firms) – 1 = lack of industrial diversity (one or few firms) Is an absolute measure; Does not take neighbors into account

25. Employment Specialization

26. Krugman Index (KI) Definition: – KI = ∑ j |a ij -b -ij | a = the share of industry j in county i’s total employment b = the share of the same industry in the employment of all other counties, -i KI = the absolute values of the difference between these shares, summed over all industries Features: – Ranges from 0 to 2.0 – 0 = county i has industrial composition identical to its comparison counties – 2 = county i has industrial composition without any similarity (no common industries) to its comparison counties Is a relative measure; Compares to one’s neighbors. It’s our choice!

27. Employment Specialization

31. Accessibility to Markets/Distance to Markets [PENDING]

32. OLS Results

46. Modeling Spatial Relationships  Inverse Distance …  K-Nearest Neighbor …  Contiguity …

47. Contiguous Counties

48. The average county has 5 to 6 neighbors (main point) How many neighbors does the…

49. Global Spatial Autocorrelation Growth rates display spatial dependence…Moran’s I…Null hypothesis

50. Own growth rates depend on neighbors (idea)

51. Main Findings

52. Future Research

53. Questions