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

Economic GrowthIN THE UNITED STATES OF AMERICA

A County-level Analysis

April HarrisElana Kaufman

Sohair OmarElizabeth Pearson

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.

Agenda

1. Theory 2. Data3. Summary Statistics4. Results5. Findings/Conclusion6. Future research/Recommendations7. Questions

What explains economic growth?

1. Neo-Classical Theory 2. Endogenous Growth Theory 3. New Economic Geography

(NEG)

Neo-Classical Theory• Long-run growth is the result of continuous technological progress, which is determined exogenously• Key implications

• Conditional convergence—if country starts from 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 (limited empirical evidence)• Leaves technological progress out of the model—technology

is modeled as an exogenously determined constant rate

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

•Developed in the 1980s as a response to criticism of the neo-classical growth model

Endogenous Growth Theory

•An economic theory which argues that economic growth is generated from within a system as a direct result of internal processes. •More specifically, the theory notes that the enhancement of a nation's human capital will lead to economic growth by means of the development of new forms of technology and efficient and effective means of production.•Theory emphasizes that private investment in R&D is the central source of technical progress•Protection of property rights and patents can provide the incentive to engage in R&D•Investment in human capital (education and training of the workforce) is an essential ingredient of growth

Endogenous Growth TheoryArrow and Sheshinksi

Knowledge is non-rival, therefore discoveries spillover to the entire economy

Romer and LucasCompetitive assumptions can be maintained and determine an equilibrium rate of technological progress but the growth rate is not Pareto optimal. At the end, growth and knowledge can increase boundlessly. No convergence is predicted.

Romer, Aghion and HowittR&D activities reward firms through monopolistic power. The equilibrium is not Pareto optimal, but rather one with monopolistic competition. The stock of human capital determines growth, but too little human capital will be devoted to R&D. Also, integration into world markets increases growth rates, and large populations are not sufficient to generate growth.

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 GeographyGeneral 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 GeographyBasis for regional divergence:

• 1st - 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.

• 2nd - localized industries can support the production of nontradable specialized inputs.

• 3rd - informational spillovers can give clustered firms a better production function than isolated producers.

New Economic GeographyRe-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 GeographyRe-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 GeographyRe-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

– 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

DataSources•Description•Frequency•Timing (1998-2008)•Sample Size (Special Cases)

Dependent variable: •Annualized per capita personal income growth (real in 1998)

Independent variables:•Log of income in the initial year (1998)•Physical capital/infrastructure•Education rates•Innovation Index•Employment rate •Employment specialization •Accessibility to Markets/Distance to Markets

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 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

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)

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

(Capture above info in bar graph)

Innovation Index

[COMING SOON]

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!)

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 SpecializationAgglomeration:

• 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 SpecializationReturning to employment specialization:

• How is it modeled?– Specialization indices

• Herfindahl Index• Krugman Index

Employment SpecializationHerfindahl Index (HI):

• Definition:– The Herfindahl index is the sum across industrial sectors of the

square of that sector’s share of employment


• 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!)


• Pros:– Captures industrial specialization

• Cons:– Is an absolute measure; Does not take neighbors into account

Employment SpecializationKrugman 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


• Pros:– Captures industrial specialization

– Is a relative measure; Compares to one’s neighbors


• 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.


• 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 SpecializationWe 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 to Markets/Distance to Markets

[PENDING]

OLS Results

_cons .0447189 .008105 5.52 0.000 .0288272 .0606106lninitiali~e -.003361 .0008151 -4.12 0.000 -.0049592 -.0017628 totalpcpig~h Coef. Std. Err. t P>|t| [95% Conf. Interval]

Total .294853157 3078 .000095794 Root MSE = .00976 Adj R-squared = 0.0052 Residual .293232788 3077 .000095298 R-squared = 0.0055 Model .001620368 1 .001620368 Prob > F = 0.0000 F( 1, 3077) = 17.00 Source SS df MS Number of obs = 3079

. reg totalpcpigrowth lninitialincome

Scatter Plot

-.05

0.0

5.1

tota

lpcp

igro

wth

9 9.5 10 10.5 11 11.5lninitialincome

OLS Results

_cons .0107438 .0002969 36.18 0.000 .0101616 .0113261high_lengt~s 1.48e-06 6.28e-07 2.35 0.019 2.46e-07 2.71e-06 totalpcpig~h Coef. Std. Err. t P>|t| [95% Conf. Interval]


. reg totalpcpigrowth high_length_miles

OLS Results

_cons .0973624 .0104257 9.34 0.000 .0769203 .1178044percentmor~s .0401284 .0028069 14.30 0.000 .0346248 .0456319percenthsd~a (dropped)percentles~s .0231712 .0035923 6.45 0.000 .0161277 .0302147lninitiali~e -.0109003 .0010488 -10.39 0.000 -.0129567 -.0088438 totalpcpig~h Coef. Std. Err. t P>|t| [95% Conf. Interval]


. reg totalpcpigrowth lninitialincome percentlessthanhs percenthsdiploma percentmorethanhs

OLS Results

_cons .0109692 .00019 57.74 0.000 .0105968 .0113417 airports .0016204 .0003464 4.68 0.000 .0009412 .0022995 totalpcpig~h Coef. Std. Err. t P>|t| [95% Conf. Interval]


. reg totalpcpigrowth airports

OLS Results

_cons .0110871 .000308 36.00 0.000 .0104832 .0116909 airports .0017429 .0004284 4.07 0.000 .0009029 .0025829high_lengt~s -3.76e-07 7.74e-07 -0.49 0.627 -1.89e-06 1.14e-06 totalpcpig~h Coef. Std. Err. t P>|t| [95% Conf. Interval]


. reg totalpcpigrowth high_length_miles airports

OLS Results

_cons .0188594 .0007721 24.43 0.000 .0173455 .0203734youthemprate -.0166641 .0016598 -10.04 0.000 -.0199186 -.0134096 totalpcpig~h Coef. Std. Err. t P>|t| [95% Conf. Interval]


. reg totalpcpigrowth youthemprate

_cons .0206125 .0016478 12.51 0.000 .0173816 .0238434totalemprate -.0134318 .0023647 -5.68 0.000 -.0180683 -.0087952 totalpcpig~h Coef. Std. Err. t P>|t| [95% Conf. Interval]


. reg totalpcpigrowth totalemprate

OLS Results

_cons .0119877 .0018895 6.34 0.000 .008283 .0156925totalemprate -.0228014 .0243243 -0.94 0.349 -.070495 .0248922workingage~e .033683 .0217282 1.55 0.121 -.0089204 .0762863youthemprate -.0204913 .0039113 -5.24 0.000 -.0281604 -.0128222 totalpcpig~h Coef. Std. Err. t P>|t| [95% Conf. Interval]


. reg totalpcpigrowth youthemprate workingageemprate totalemprate

_cons .0191127 .0017728 10.78 0.000 .0156367 .0225888workingage~e -.0107746 .0024347 -4.43 0.000 -.0155485 -.0060007 totalpcpig~h Coef. Std. Err. t P>|t| [95% Conf. Interval]


. reg totalpcpigrowth workingageemprate

OLS Results

_cons .0090671 .0004528 20.02 0.000 .0081792 .009955 ki .0027874 .0005197 5.36 0.000 .0017685 .0038063 totalpcpig~h Coef. Std. Err. t P>|t| [95% Conf. Interval]


. reg totalpcpigrowth ki

_cons .0104854 .00032 32.76 0.000 .0098579 .0111128 hi .0034814 .0011334 3.07 0.002 .0012591 .0057037 totalpcpig~h Coef. Std. Err. t P>|t| [95% Conf. Interval]


. reg totalpcpigrowth hi

OLS Results

_cons .0173707 .0015157 11.46 0.000 .0143988 .0203426accessibil~y -.00061 .0001514 -4.03 0.000 -.000907 -.0003131 totalpcpig~h Coef. Std. Err. t P>|t| [95% Conf. Interval]


. reg totalpcpigrowth accessibility

OLS Results

_cons .0042186 .0006293 6.70 0.000 .0029846 .0054525distance_t~t 8.36e-14 7.14e-15 11.71 0.000 6.96e-14 9.76e-14 totalpcpig~h Coef. Std. Err. t P>|t| [95% Conf. Interval]


. reg totalpcpigrowth distance_to_market

OLS Results

_cons .0634634 .0111748 5.68 0.000 .0415525 .0853743 airports .0011786 .0004263 2.76 0.006 .0003428 .0020143workingage~e .0025639 .0043352 0.59 0.554 -.0059364 .0110642youthemprate -.0116003 .002529 -4.59 0.000 -.0165589 -.0066417 ki .0027824 .000657 4.23 0.000 .0014941 .0040706lninitiali~e -.0068983 .0011938 -5.78 0.000 -.0092391 -.0045576percentmor~s .0277121 .0031681 8.75 0.000 .0215003 .0339239percentles~s .0095969 .0041919 2.29 0.022 .0013777 .0178161high_lengt~s -4.46e-07 7.87e-07 -0.57 0.571 -1.99e-06 1.10e-06distance_t~t 4.25e-14 8.05e-15 5.28 0.000 2.67e-14 5.82e-14 totalpcpig~h Coef. Std. Err. t P>|t| [95% Conf. Interval]


OLS Results

_cons .0631671 .0111864 5.65 0.000 .0412335 .0851007workingage~e .0020258 .0043356 0.47 0.640 -.0064751 .0105267youthemprate -.0117032 .0025314 -4.62 0.000 -.0166667 -.0067397 ki .0025355 .0006516 3.89 0.000 .0012577 .0038132lninitiali~e -.0068954 .0011951 -5.77 0.000 -.0092386 -.0045521percentmor~s .029272 .0031208 9.38 0.000 .0231529 .0353911percentles~s .010242 .0041899 2.44 0.015 .0020267 .0184573high_lengt~s 5.45e-07 7.02e-07 0.78 0.437 -8.31e-07 1.92e-06distance_t~t 4.20e-14 8.05e-15 5.22 0.000 2.62e-14 5.78e-14 totalpcpig~h Coef. Std. Err. t P>|t| [95% Conf. Interval]


> i youthemprate workingageemprate

OLS Results

_cons .0762806 .0110451 6.91 0.000 .0546241 .0979372 airports .0009525 .0004248 2.24 0.025 .0001196 .0017853workingage~e .007201 .0043019 1.67 0.094 -.001234 .0156359youthemprate -.0144924 .0025074 -5.78 0.000 -.0194087 -.0095761 hi .0007522 .0012876 0.58 0.559 -.0017724 .0032769lninitiali~e -.0081632 .0011864 -6.88 0.000 -.0104894 -.0058371percentmor~s .0267372 .0031696 8.44 0.000 .0205225 .032952percentles~s .0102764 .0042013 2.45 0.015 .0020388 .018514high_lengt~s -1.22e-06 7.77e-07 -1.57 0.118 -2.74e-06 3.07e-07distance_t~t 4.67e-14 8.02e-15 5.82 0.000 3.10e-14 6.24e-14 totalpcpig~h Coef. Std. Err. t P>|t| [95% Conf. Interval]


OLS Results

_cons .0635042 .0113212 5.61 0.000 .0413063 .085702 airports .0012035 .0004274 2.82 0.005 .0003654 .0020416totalemprate -.0089656 .0036322 -2.47 0.014 -.0160873 -.0018438 ki .0036642 .0006237 5.88 0.000 .0024413 .004887lninitiali~e -.0068724 .0012303 -5.59 0.000 -.0092848 -.0044601percentmor~s .0300993 .0031391 9.59 0.000 .0239443 .0362543percentles~s .0117685 .0042226 2.79 0.005 .0034891 .0200479high_lengt~s -3.64e-07 7.90e-07 -0.46 0.645 -1.91e-06 1.18e-06distance_t~t 4.55e-14 8.02e-15 5.67 0.000 2.98e-14 6.12e-14 totalpcpig~h Coef. Std. Err. t P>|t| [95% Conf. Interval]


Modeling Spatial Relationships

Inverse Distance…

K-Nearest Neighbor…

Contiguity…

Contiguous Counties

The average county has 5 to 6 neighbors (main point)How many neighbors does

the…

1 2 3 4 5 6 7 8 9 10 11 12 13 140

200

400

600

800

1000

1200

Number of Contiguous Neighbors

Number of Neighbors

Num

ber

of C

ount

ies

Global Spatial Autocorrelation

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

*1-tail test totalpcpigrowth 0.432 -0.000 0.010 41.176 0.000 Variables I E(I) sd(I) z p-value* Moran's I

Row-standardized: NoType: Imported (binary)Name: W Weights matrix

Measures of global spatial autocorrelation

. spatgsa totalpcpigrowth, weights(W) moran

Own growth rates depend on neighbors (idea)

Moran scatterplot (Moran's I = 0.439)totalpcpigrowth

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1952

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16719491443381

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278

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705

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190514465902458

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1990

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13031865298830217491143860180

768

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1975

230284903

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5509572181175102997

2810

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618

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13801883164

1609

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1574

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1593

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977

1566

136728831586

8871706

1285

1432

137417581056

23412299

1422

2775

9561716

250123501973

1950

1891798

1763

485

127

23601732966128214662870

2137

29992219

632

1526

18392889

21

75529495371053138520041958

547

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109521472780

229

17531580

1663

2238509

206545

1061

2466

51

1145

577

517

2167258

979

1994

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2400

209721162274249226812954871

1017

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632717

6362278

2326

1572

2789

18541071

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297528392930

908

2794

13692281

914

2602

1995

2931400

21181138

1587

1296

25612117

21851005

1575

1363

1570

2309

853845

118713992787

1553

1971

349

2155

11562535

947

28632907

2737

2126

9282858

2743795

2817

148

27119677

2233023

24952940536221113216961336

2347

1038

240

396

340

2886

1853

24931161

2390

1681556

377

1992

1185

782

1793

1998

2846

1081

2866

2689

2144

1403

11912670

1600

2882112010831175

54

244

23522646

1942190325991495235

253610992515

1726

1195

1408

2384

2782

1963

1094

3003

1972

51828072755

1127

2984

10862514

1902222

2184

8691411

1194

2690

2288

1607

17772578

1182

3058

1142

822

23781841132

1169

1131

27412142

2542

2423540

2978

2695

1168

2610

205

1766

24742796

1664

2756

1608

916

348

53426351025

22918

2582

154

1320

801227

1595

2381985

1791

841

2615

1338

3722441135

1748

200

774

1986

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2106

2772

272827772630

2874

2666

14121746

2888

2606

111915902971

158

197219448

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2976

187

159

559

1125

1703

2773759837

2161

2855

5261582

2120

2353

1564

1170

23812785

2481

43

1967

15851618

520226011152972

2128

227271

2457

1559212

1578

178779116542114

2541

3042

14292132781

129

333

1055

109716042692

554

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1565

2164

1264

249

256

2129

2885

2598455

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1844850858

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2563

2159

2758

1171531421520

11732559

2731

2742

1734

159926212634

767

2792

247028871396

1651

2377

1713465

14261623

1019

23611991

11092860

281429682332

13662005279

2099

17202300

11932132

2290

2589

1376

1588

2631

46370

1301

1128

291837

1614

1113

2385

1091

886

835

2906

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989

1783

1785

1419

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1108

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107

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2683

741347

25272328

12118529601602412783

2371

26051923

325

2256

1596

879

2580136

2343

1701921

2002

71782332

11172648

30642650

21683362133

1568

1088

1398210316112524

2491

2110

2556

27021571

2569

1584

3072

11111764

110228811106

145

2502

224

1788095252908

15792127

2980

19572

1317

1968

1597

2312354

2637

230322791105

1130

17891907

2006

158311262152

1075

21111778863

2344198

2836

308

2158

2962

21501160

2337

277923392590

1104

1589

2842335

2626

1981

2661

2824

1615

2804

253255

186

2157

2201

15771093

1636

1966

1390

2342

2594

23301728

2308

2113

1434

2112

1956

1581

1082

23861790199

18162456

2277

2696

152

2618

2624

6128222289

16102625

1786

16782880

25922528

1881

2346109615942375

21212657

2693

985

159821312512

170227051141272

1101

11722154

27212329

69

1999

1402

2686

2135

21082725

2373

1129

2160

1983

25641922

2140

2173

1969

1980

20032671

2336

2691613

2612

446

198427263070

356

2165

2653

30571957

73

2540

523

2153

1974

261

1982

27331178

2333

2539

1133

2340

301

1069320

1617290

2504306768

20002704

557

19962363

2623

183

2356

19652555

1576

2163

3063

260720072652

26132641

2367

2351

3075

21462382

2537

2557

934

21512554

1769

3078

1124383322603

2629

1979

264

1997

21012198884

26971107

350

1605

2671774

23492739

156926473071

1970

225

3060

30662719

2552

1988

200121392573

3061

307333424901960

196119763079

1136

2138

2749

2736

10902538

3065

1122

3069

2766

1962

3059

1592

15331624

2358306219933068

1717

20081959

1394

1116

30771978

1389

2639

1622

1114

2706

1011

2505

25753076

2680

3074

Main Findings

Future Research

Questions

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

Documents

regional economic growth

growth model

growth rates

economic convergence

economic growthin

higher growth rate problems

new economic geographyw

human capital education