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 Key assumptions Key Factors

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 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 factors Economies of scale Transportation costs Location of demand 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 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 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 Index Employment rate Employment specializationCensus of Employment and Wages 1998-2007 Accessibility to Markets/Distance to Markets

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. Physical Capital/Infrastructure Source: ESRI Data and Maps 9.3 Media Kit (2008) Density of major roads by county Airports by county (Visual representation? Example: Frequency table of airports)

18. 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)

19. Innovation Index [COMING SOON]

20. 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!)

21. Employment Specialization What is it? – Measure of industrial concentration of a region (county) What is it meant to capture? – Meant to capture notion of agglomeration

22. Employment Specialization Agglomeration: What is it? – The spatial concentration of industry – A determinant of economic growth in NEG growth theory How is it modeled? – Employment specialization proxies for agglomeration

23. Employment Specialization Returning to employment specialization: How is it modeled? – Specialization indices Herfindahl Index Krugman Index

24. Employment Specialization Herfindahl Index (HI): Definition: – The Herfindahl index is the sum across industrial sectors of the square of that sector’s share of employment

25. Employment Specialization Herfindahl Index (HI): Features: – 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) FYI: – Used in determinations of market share in regulation of monopolistic activity (Replace text with math!)

26. Employment Specialization Herfindahl Index (HI): Pros: – Captures industrial specialization Cons: – Is an absolute measure; Does not take neighbors into account

27. Employment Specialization 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

28. Employment Specialization Krugman Index (KI): 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

29. Employment Specialization Krugman Index (KI): Pros: – Captures industrial specialization – Is a relative measure; Compares to one’s neighbors

30. Employment Specialization Krugman Index (KI): Cons: – Whether agglomeration economies can be fully captured by relative concentration measures, or whether the absolute size of economic clusters is best for understanding the effects of geographical concentration on economic growth is debatable. – Argued that the absolute size of clusters should be the basis for calculating the level of specialization. – Objections hold that this level is systematically underestimated for larger metropolitan areas when relative levels of concentration are used.

31. Employment Specialization Krugman Index (KI): Cons: (cont’d) For a review/discussion of the literature on this point: – Drennan, M. and Lobo, J. (2007) Specialization Matters: the Knowledge Economy and United States Cities.” Los Angeles: UCLA School of Public Affairs, unpublished manuscript. – Duranton J. and Puga D. (2003) Micro-foundation of urban agglomeration economies in Henderson V. J. and Thisse JF. (eds) Handbook of Regional and Urban Economics Vol.4 Cities and Geography, Amsterdam: Elsevier.

32. Employment Specialization We chose… Because of our specific interest in why regions grow at different rates relative to one another, the comparative nature of the Krugman Index seems better suited to our needs than the Herfindahl Index.

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

34. OLS Results

35. Scatter Plot

36. OLS Results

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

50. Contiguous Counties

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

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

53. Own growth rates depend on neighbors (idea)

54. Main Findings

55. Future Research

56. Questions