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

Objective To understand the factors driving differences in regional economic growth across the United States. How important are stocks of physical, human, and intellectual capital in explaining these differences? What role do local labor market conditions play? Do agglomeration economies or geographic characteristics drive differences in economic performance across regions?

Agenda 1.Theory 1.Neo-Classical Theory 2.Endogenous Growth Theory 3.New Economic Geography (NEG) 2.Variables 3.Dependent variable: Per capita personal income 4.Independent variables 1.Physical capital/infrastructure 2.Education rates 3.Innovation Index 4.Employment rates 5.Employment specialization 6.Accessibility 5.OLS results 6.Spatial results 7.Main findings 8.Future research 9.Questions

Neo-Classical Theory Long-run growth is the result of continuous technological progress, which is determined exogenously Key implications Lack of technological change would stop growth because of diminishing returns to capital Capital accumulation is also determined by the savings rate and the rate of capital depreciation Conditional convergence—if a country starts from a lower level of per capita output, it’s expected to have a higher growth rate Problems Predicts economic convergence, which hasn’t been seen empirically Incapable of explaining long-run growth in the absence of technological improvement Leaves technological progress out of the model—technology is modeled as an exogenously determined constant rate

Endogenous Growth Theory

New Economic Geography What is economic geography? – Economic geography is the location of factors of production in space (Krugman 1991)

New Economic Geography What does NEG seek to answer? – Why and when does manufacturing become concentrated in a few regions, leaving others relatively undeveloped? – In order to realize scale economies while minimizing transport costs, manufacturing firms tend to locate in the region with larger demand, but the location of demand itself depends on the distribution of manufacturing. Emergence of a core-periphery pattern depends on transportation costs, economies of scale, and the share of manufacturing in national income.

New Economic Geography How does NEG theory answers these questions? – Through a model of geographical concentration of manufacturing based on the interaction of economies of scale with transportation costs – Concentration of manufacturing in one location need not always happen and that whether it does depends in an interesting way on a few key parameters

New Economic Geography General Model: Two industrial sectors – Agriculture and manufacturing; agriculture fixed while manufacturing is mobile Transport costs: – low benefits manufacturing to aggregate – High does not benefit manufacturing to aggregate – Technology (specifically transportation technology) lowers transport costs – There exists tipping point between high and low transport costs

New Economic Geography What is the tipping point? – Low transport costs and external economies of scale increase the income of the core (urban) region relative to its periphery. – Agglomeration raises wages in the core region relative to the periphery. – If costs fall far enough (wages increase enough), the wage differential will induce firms to relocate back to peripheral regions.

New Economic Geography If one region has right mix of key factors before - even just slightly before - another similar region, the leading region takes off and the other may not grow. Thus despite early similarity regions can become quite different. Key factors are: – transport costs – proportion of economy in manufacturing (affects ability to take advantage of economies of scale) – population

New Economic Geography Basis for regional divergence: 1 st - the concentration of several firms in a single location offers a pooled market for workers with industry-specific skills, ensuring both a lower probability of unemployment and a lower probability of labor shortage. 2 nd - localized industries can support the production of nontradable specialized inputs. 3 rd - informational spillovers can give clustered firms a better production function than isolated producers.

New Economic Geography Re-statement of general model: Agricultural production is characterized both by constant returns to scale and by intensive use of immobile land. The geographical distribution of this production will therefore be determined largely by the exogenous distribution of suitable land. Manufacturing is characterized by increasing returns to scale and modest use of land.

New Economic Geography Re-statement of general model: (cont’d) Because of economies of scale, production of each manufactured good will take place at only a limited number of sites. – Other things equal, the preferred sites will be those with relatively large nearby demand, since producing near one's main market minimizes transportation costs. Other locations will then be served from these centrally located sites.

New Economic Geography Re-statement of general model: (cont’d) As transportation costs decrease and economies of scale are present, a region with a relatively large non- rural population (or larger initial production) will be an attractive place to produce because of the large local market and because of the availability of goods and services produced there. – 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.

New Economic Geography How does NEG relate to this research? – NEG seeks to explain concentration and dispersion of economic activity – Restated, NEG seeks to explain differentials in economic activity – this is precisely what we want to know!

New Economic Geography How does NEG differ from Neo-Classical and Endogenous growth theories? – NEG takes scale into account – Neo-Classical and Endogenous growth theories are only concerned with what happens at the margins

New Economic Geography How does NEG differ from Neo-Classical and Endogenous growth theories? (cont’d) – 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

Variables Dependent variable: Per capita personal income Independent variables: Physical capital/infrastructure Education rates Innovation Index Employment rate Employment specialization Accessibility

Per Capita Personal Income Source: Bureau of Economic Analysis 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

Physical Capital/Infrastructure

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

Innovation Index

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%

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

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

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

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

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

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

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

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

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

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.

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.

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.

Accessibility

OLS Results

Spatial Results

Main Findings

Future Research

Questions