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Economic Growth IN THE UNITED STATES OF AMERICA A County-level Analysis. April Harris Elana Kaufman Sohair Omar Elizabeth Pearson. Objective. To explore the factors driving differences in regional economic growth across the United States.

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economic growth in the united states of america a county level analysis

Economic GrowthIN THE UNITED STATES OF AMERICA A County-level Analysis

April Harris

Elana Kaufman

Sohair Omar

Elizabeth Pearson

slide2

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.
slide3

Agenda

  • Theory
  • Data
  • Summary Statistics
  • Results
  • Findings/Conclusion
  • Future research/Recommendations
  • Questions
slide4

What theories explain economic growth?

  • Neo-Classical Theory
  • Endogenous Growth Theory
  • New Economic Geography (NEG)
slide5

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)
slide6

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
slide7

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)
slide8

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.
slide9

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
slide10

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
slide11

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
slide13

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
slide17

Infrastructure

  • A measure of Physical Capital.
    • Mileage of major roads by county
    • Airports by county
slide20

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)
slide21

Innovation Index

[COMING SOON]

slide22

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!)
slide23

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
slide24

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)
slide25

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
slide43

Modeling Spatial Relationships

  • Inverse Distance
  • K-Nearest Neighbor
  • Contiguity
slide46

Global Spatial Autocorrelation

Growth rates display spatial dependence…Moran’s I…Null hypothesis