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

1. Economic GrowthIN THE UNITED STATES OF AMERICA A County-level Analysis April Harris Elana Kaufman Sohair Omar Elizabeth Pearson

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 • Theory • Data • Summary Statistics • Results • Findings/Conclusion • Future research/Recommendations • Questions

4. What theories explain economic growth? • Neo-Classical Theory • Endogenous Growth Theory • 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. New Economic Geography: Why is manufacturing concentrated in a few regions? • Economic geography: the location of factors of production in space • Key Implication • Despite early similarity regions can become quite different! • Key factors causingagglomeration or dispersion • Economies of scale • Transportation costs • Location of demand • Population

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

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 growth1/10 * 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: • NΣi=1 s2 • 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. Employment Specialization • Krugman Index (KI) • Definition: • KI = ∑j|aij-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

28. Employment Specialization

29. Employment Specialization

30. Employment Specialization

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

32. OLS Results

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45. 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)