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 factors • Economies of scale • Transportation costs • Location of demand • Population

10. New Economic Geography predicts that the right mix of key factors causes growth • Key implications • Agglomeration raises wages in the core region relative to the periphery • Despite early similarity regions can become quite different • Large nearby demand causes more growth • Producing near one’s main market minimizes transportation costs

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: Measure of Industrial Concentration of Region • Meant to capture notion of agglomeration • The spatial concentration of industry • A determinant of economic growth in NEG growth theory. • How is it modeled? • Specialization indices • Herfindahl Index • Krugman Index

24. Herfindahl Index • The Herfindahl index is the sum across industrial sectors of the square of that sector’s share of employment • Ranges from 0 to 1.0 • 0 = large number of very small firms • (perfect competition) • 1 = a single monopolistic producer • (complete monopoly by a single firm)

25. Krugman Index • 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 • 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

26. To Measure Employment Specialization We Chose the Krugman Index

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

28. OLS Results

29. Scatter Plot

30. OLS Results

31. OLS Results

32. OLS Results

33. OLS Results

34. OLS Results

35. OLS Results

36. OLS Results

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38. OLS Results

39. OLS Results

40. OLS Results

41. OLS Results

42. OLS Results

43. Modeling Spatial Relationships • Inverse Distance • … • K-Nearest Neighbor • … • Contiguity • …

44. Contiguous Counties

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

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

47. Own growth rates depend on neighbors (idea)

48. Main Findings

49. Future Research

50. Questions