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. Data

3. Summary Statistics

4. Results

5. Findings/Conclusion

6. Future research/Recommendations

7. Questions

What theories explain economic growth?

1. Neo-Classical Theory

2. Endogenous Growth Theory

3. New Economic Geography

(NEG)

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)

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

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)

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.

• Economic geography: the location of factors of production in space

• Key Implication• Despite early similarity regions can become quite different!

• Key factors causing agglomeration or dispersion1. Economies of scale 2. Transportation costs3. Location of demand4. Population

New Economic Geography: Why is manufacturing concentrated in a few

regions?

New Economic Geography predicts that the right mix of key

factors causes growth• How does differentiation occur?

• General NEG model answersOne region slightly larger

+transportation costs

+ IRS+

larger initial production=

more people & production spatially close together

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.

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

We obtained data on 3,079 counties between 1998-2007

Variable Source Year(s)

Annualized per capita personal income growth

Bureau of Economic Analysis

1998-2007

Log of income in the initial year

Bureau of Economic Analysis

1998

Physical capital/infrastructure

ESRI Data and Maps 9.3 Media Kit

2008

Education rates U.S. Census 2000

Innovation Index Economic Development Administration

2008

Employment rate Bureau of Economic Analysis

1998-2007

Employment specialization Census of Employment and Wages

1998-2007

Accessibility to Markets/Distance to Markets

ESRI Data and Maps 9.3 Media Kit Bureau of Economic Analysis

2008

1998

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

Legend

Per Capita Personal Income 1998

income_1998

$7,756.00 - $17,986.00

$17,986.01 - $21,883.00

$21,883.01 - $26,732.00

$26,732.01 - $35,888.00

$35,888.01 - $76,450.00

Per Capita Personal Income By County 1998

Legend

Per Capita Personal Income 2007

income_2007

$6,777.41 - $20,522.62

$20,522.63 - $25,391.39

$25,391.40 - $32,690.20

$32,690.21 - $47,484.53

$47,484.54 - $104,855.10

Per Capita Personal Income By County 2007

Legend

Total PCPI Growth

1998-2007

-0.035588 - 0.002768

0.002769 - 0.011047

0.011048 - 0.019209

0.019210 - 0.031769

0.031770 - 0.070344

Total Per Capita Personal Income Growth Rate By County 1998-2007

Infrastructure•A measure of Physical Capital.

• Mileage of major roads by county

• Airports by county

Major Road Mileage by County

01

002

003

004

00F

req

uenc

y

0 1000 2000 3000 4000 5000Major roads in miles

high_lengt~s 3079 380.8334 280.9239 32.13309 4584.723 Variable Obs Mean Std. Dev. Min Max

. sum high_length_miles

Number of airports by County

Total 3,079 100.00 6 1 0.03 100.00 4 7 0.23 99.97 3 8 0.26 99.74 2 62 2.01 99.48 1 458 14.87 97.47 0 2,543 82.59 82.59 airports Freq. Percent Cum. Number of

05

001

000

150

02

000

250

0N

umb

er o

f Cou

ntie

s

0 1 2 3 4 5 6Number of airports

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?– Captures notion of agglomeration

– What is agglomeration?The close spatial concentration of industryA determinant of economic growth in NEG growth

theory – How is it modeled?

Specialization indices• Herfindahl Index• Krugman Index

Employment SpecializationHerfindahl Index (HI)

• Definition:– NΣi=1 s2

• Features:– Ranges from 0 to 1.0

– 0 = industrial diversity (lots of firms)

– 1 = lack of industrial diversity (one or few firms)

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

Employment Specialization

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 • Is a relative measure; Compares to one’s neighbors. It’s our choice!

Employment Specialization

Employment Specialization

Employment Specialization

Employment Specialization

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

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]

Total .294853157 3078 .000095794 Root MSE = .00978 Adj R-squared = 0.0015 Residual .294323761 3077 .000095653 R-squared = 0.0018 Model .000529396 1 .000529396 Prob > F = 0.0187 F( 1, 3077) = 5.53 Source SS df MS Number of obs = 3079

. 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]

Total .294853157 3078 .000095794 Root MSE = .00942 Adj R-squared = 0.0737 Residual .272867456 3075 .000088737 R-squared = 0.0746 Model .0219857 3 .007328567 Prob > F = 0.0000 F( 3, 3075) = 82.59 Source SS df MS Number of obs = 3079

. 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]

Total .294853157 3078 .000095794 Root MSE = .00975 Adj R-squared = 0.0067 Residual .292771168 3077 .000095148 R-squared = 0.0071 Model .002081988 1 .002081988 Prob > F = 0.0000 F( 1, 3077) = 21.88 Source SS df MS Number of obs = 3079

. 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]

Total .294853157 3078 .000095794 Root MSE = .00976 Adj R-squared = 0.0065 Residual .29274868 3076 .000095172 R-squared = 0.0071 Model .002104477 2 .001052238 Prob > F = 0.0000 F( 2, 3076) = 11.06 Source SS df MS Number of obs = 3079

. 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]

Total .294853157 3078 .000095794 Root MSE = .00963 Adj R-squared = 0.0314 Residual .285500966 3077 .000092785 R-squared = 0.0317 Model .00935219 1 .00935219 Prob > F = 0.0000 F( 1, 3077) = 100.79 Source SS df MS Number of obs = 3079

. 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]

Total .294853157 3078 .000095794 Root MSE = .00974 Adj R-squared = 0.0101 Residual .291793528 3077 .000094831 R-squared = 0.0104 Model .003059628 1 .003059628 Prob > F = 0.0000 F( 1, 3077) = 32.26 Source SS df MS Number of obs = 3079

. 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]

Total .294853157 3078 .000095794 Root MSE = .00961 Adj R-squared = 0.0359 Residual .283988615 3075 .000092354 R-squared = 0.0368 Model .010864542 3 .003621514 Prob > F = 0.0000 F( 3, 3075) = 39.21 Source SS df MS Number of obs = 3079

. 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]

Total .294853157 3078 .000095794 Root MSE = .00976 Adj R-squared = 0.0060 Residual .292988405 3077 .000095219 R-squared = 0.0063 Model .001864752 1 .001864752 Prob > F = 0.0000 F( 1, 3077) = 19.58 Source SS df MS Number of obs = 3079

. 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]

Total .294853157 3078 .000095794 Root MSE = .00974 Adj R-squared = 0.0089 Residual .29212165 3077 .000094937 R-squared = 0.0093 Model .002731506 1 .002731506 Prob > F = 0.0000 F( 1, 3077) = 28.77 Source SS df MS Number of obs = 3079

. 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]

Total .294853157 3078 .000095794 Root MSE = .00977 Adj R-squared = 0.0027 Residual .29395184 3077 .000095532 R-squared = 0.0031 Model .000901317 1 .000901317 Prob > F = 0.0021 F( 1, 3077) = 9.43 Source SS df MS Number of obs = 3079

. 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]

Total .294853157 3078 .000095794 Root MSE = .00976 Adj R-squared = 0.0049 Residual .293306191 3077 .000095322 R-squared = 0.0052 Model .001546966 1 .001546966 Prob > F = 0.0001 F( 1, 3077) = 16.23 Source SS df MS Number of obs = 3079

. 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]

Total .294853157 3078 .000095794 Root MSE = .00958 Adj R-squared = 0.0424 Residual .282271316 3077 .000091736 R-squared = 0.0427 Model .01258184 1 .01258184 Prob > F = 0.0000 F( 1, 3077) = 137.15 Source SS df MS Number of obs = 3079

. 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]

Total .294853157 3078 .000095794 Root MSE = .00926 Adj R-squared = 0.1052 Residual .263075644 3069 .00008572 R-squared = 0.1078 Model .031777513 9 .003530835 Prob > F = 0.0000 F( 9, 3069) = 41.19 Source SS df MS Number of obs = 3079

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]

Total .294853157 3078 .000095794 Root MSE = .00927 Adj R-squared = 0.1032 Residual .263730976 3070 .000085906 R-squared = 0.1056 Model .031122181 8 .003890273 Prob > F = 0.0000 F( 8, 3070) = 45.29 Source SS df MS Number of obs = 3079

> 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]

Total .294853157 3078 .000095794 Root MSE = .00929 Adj R-squared = 0.1000 Residual .264583407 3069 .000086212 R-squared = 0.1027 Model .030269749 9 .003363305 Prob > F = 0.0000 F( 9, 3069) = 39.01 Source SS df MS Number of obs = 3079

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]

Total .294853157 3078 .000095794 Root MSE = .00929 Adj R-squared = 0.0997 Residual .26478041 3070 .000086248 R-squared = 0.1020 Model .030072747 8 .003759093 Prob > F = 0.0000 F( 8, 3070) = 43.58 Source SS df MS Number of obs = 3079

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

Counti

es

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

Wz

z-5 -4 -3 -2 -1 0 1 2 3 4 5 6 7

-3

-2

-1

0

1

2

3

4

5

228 2707

2287

2591

17722362

870

2668

2236323

2673

361

2586

1677

491

206635925471858

2471711419

46424521861

424

1221

9251662

391

221265

2644

440

2098

17712902

691

277

564

2166

1721

245320471243866

61123102196

22822443

2029

1634

10526835114251694

906670

14312531

738

1723

12442019

2544434

1000

1226

17401068

432

1393

2631648

1643

1275

1197

4152025

679

9981873388

1722

2901881

1627

513360

31310131406303530

376

2628

1240

2102

7039461890

804405

69660710641044

1270

132224421350

458

993384963

2369

494

2767

7145792018

2873

1246437

20411063

125420111687

2583

661

1856

4033642061

6951204

6332427

502

27659215171884969

1269

2027220453

1895

3892903

1931

14521637203714542912

39421091911

270

386

1657

268712661255

1492

7191225900

101

2094

9072031

479

1641

375

430468

664

1327680

1705

961

1274

2086894694

246752373

1989

416

2028

9554451656

262

6711209

314

697

1908

2421

2869

123496743530382062

2747

1954

535

3781869480

24104562042

2482

454

12121054

515

2020

1562411

1242

2520

19381319

395

2776

2432

253

1357

1955400

1008362

2420

1205

1027

22005801479

1103

931

2093

366

978

16881201

712

9641904

1725

2425301314011428966

1007510

146

1219

640569

358

25215142993

2620

2024

909

754

1860

421431891

2078

976243118943652433392

1649

26992096

16691236

915

1868

2577

369

913

20221217

1042751

2600

2526

2084

2734

674

9911222

973

851

1015

2551

2023

259

84

14512076

1272

60814972305

889

706

147010021265

20143901507

312

813

708

1284

1206

10591256

2054939

2468

8651073436

1278

12712043385

1344802

483

2478

2034

1257

1554877716583

1199

2298113

1228

6316671417

1277

315

1251

20801230

8681684

203

2063

2558

486

1896

248

2364655

2180

15061286130512921026

1941

355

960

615

2124

20691045473

136067

3242862

1258605256528252090678439

12032307

162048920744826061066

15471562051

2095

1505

2663

2056

933

663

922

1253

9322762486709

74228911885

1877

1049

1001

20606992304

1353

2068

1621

41341019002070

972

457

944

527

2408

823

7411235753

10242745

1010

2601

2172

1444

1943

2820

594

19401889

2898512

1639

144816455126

2087

1918

367

498470490

1467

20822040

2207

12791216

2459

15222017

2765

68520792178

2525

673735

23961449

1650

1259

339

549150

1437

2055

12312753575

208373088220771947

1820962

18671208

25

2233

1665

1245

734

2089

1386

29162754

2788

11987312896

505

1878

2464

22511891

1857689

28531563

404

12391492229649

923

1795604

1268

9902932

1528

1215

1288266371120

20652012

628

1644

196

8982617

902

3172215

70229008431489

1295

1384811712

14721719

598191924221043

402

1916

5872402492273

2715

24341202516

16523682316

677

216

170

2195571

1930

2266

1934

1792

30362234155013371655

111

2744

747

1543

12631250

426

14181819721859

896

2494

499

1549

23238401462

478

471

2497

878

622

497

852

2013

294

316

1358

6652285

17332269623

983720

8851009

30141898

2608208

12491048

397

462

2444407

1892

1660

1527

666

260

1917

968

1407

2838

2904

658227022263050

2852

2982

4382048

444625

780924

3049484995

143628291029

1252

6271371880

2050

707

2698

2394

1685

710

1812

1237

1192621

1840

90458212671935

24991037

2684

687

294730392438

1447

13432516629

1034

1485

2761

30221921491

1388

1762

13832415

503

2989

892

2030

616

9812805

2450

401

2010

2769617644

1850901

2449652

2203

739

21001866

42213451661

2245

2268

1200

14422398

1913

2419292174

2619

210

1825

874207

351

8113040

406

300183826361314

2399169129113012

548

14531843

442

1531

1213941

3008

595

1936

299822301181545

2627

13652461

936341

2091

1481

2088

1511

568

2662

23955963024248022

382254

2038686

298725514733032

723

2370

2770

1512

1591

2331

157570

18002649163117451004134919282292450

1355

56728012467

82

2016

1312

148729942387

129714583791503

1630

614

13091556

13077581475

1915

13812847

2263

1518

1384

1767770

190

1561

127619392311746

47530271021

2085

1784

148812072039

954

2067

15411368

31

970810

3037

2379

22102460292919251831

2130

1674477

2560

74521751477

276230071852380183020456192015

287

1214

286

3033

409

488

1224

2830

1247

1318

18998953051

12801683

2072

1821

182321

399

6511496

2315

64711019483045105018882983

2851

562

2242

1864

717

11511754597872243

25002529

1700

2259

1987

15163011

529

2294

239

460

90

659

129817971315

2752

104974

11471439672

469

3006145926

19063016

765

124131

68228261535

30202416

414

5853035

2271

646418

1098

2823

1863

1887

555

14902568

2768

1875

1229

5866691509

220687

1273

22642261

938

2429591

1811

1699

2465182

2186

18011555

578

2645

3004181

2771

172

2123

2075

2640

798137522832026

7751775

1530692

88

9454721378

1035512938

1874818

9

2036

29092273140

3

22313025

2957

1846781

2053

222184687520092248

352

3000164619331427219715042189

999

18721328953

1912

2071

2913

2058581

2967

2265

1945

21042905

256229451291125

6091310750

1672

21761461146911461653

726

1465

732

20921162915

26141805

711

808

29251932

1756693

2546

1602

3054

1552

1675610

22671632

2854

15017731829

1946

1828621870

1859221779263424731033917

30997

408

14561546

417

2894

2476

1405

10702237

105725955893019

639

1464

449

2389

2059

16732685

2498

65

6568738556413222440508

1560737

15151016

700

2057

2306764

192

1826104118971261

2593

170724857448992257

951662834

217785297

2049

85

722

718

620190913912106909421352

193

56237943

1423

2033

558

1362

217745117

495

816

2793

428

1626

1080

292214802798

236

532

2523

728

2679

948

1382

387

1148

1468

2334

821

433

1425

2235

1686

119

1484820

13211370307

34

952

24481822221424472426

786

1841

2506

329

14783211689

2956

2935

346

13042044

2566

2981834

2222

2579

912

8072868

2843

1060

805

1514

7135522803

2750736

507

1832

1074

9112835

27865422985

23201281

935

1886

3010

39959

177314452828

1046

2534

2943

1486

3043

1081730

2664

2791

3001

2549

811676

2571

297362412992252

7761521

15367602333031

11552865

2227412

174

98824114472944

1121

1308

1540

1510137317603532258533

1670

9582204

1134

304712182216

363

573

1814

29312145

1335

2286

246212231287

2372

30221836

2409

2742587769

2327

28561176

47683175

1051

1776357

2724293415252879

2585

81767522202472

1761

538

2604

725

930701

2313

208162627485842322635

888

1889751718

563

89

48466

1076

1084

7963048

1788219011502959

1012

2193

8492763

2247

1441

5882253

1606

2407192697

1499

1227715

17361435

12

1625

2757

304

305698

2897

14948542488654

19202811

2655

2405724

2841

992

8831929

19

3015

374

1072

211

188250

1557

1638

2209

1283115776

602441

2393

1695

18551804

1977

2199

1339

1680398

84787642

2941546890

2759

1165

8971799

11632483757684657

15021815

2376

937

2584

2682899

26948481379

572

826784

830

1532

1698

1731304679923832974

648561

2727

2046

950

166624513021642

2923

49169141

729

861155187914982021

2939

42916921377

129024241693

994103528135191330

2926

8032244

1326

2224643965

2073

1077

3026

2833

1953601

1463111221791724

254847

2819

2250

6

1952

23012182300228081047

2964

1313

2397

2672

2809

30552751544

16719491443381

2317

278

2700

705

3271851

1848

2910

214

1727

6122194

2852553

6812795704

2991

7871061433

242762

2484783

2148

833

105984839

250

24301690949

14

1262

2643

8421359

2239

566

2977

401542

343

2714

26788932996

2513

1519

504

1177

4611455

271691

15246032812

1835920

1802

1862

92717141356932

1735

28162892

1880

790

13162335

1189

1482

147

292822462052

1416

1635

2712

15081460

18061658

94

182728672751

459

17572857

14041765247772724061233005

1910

1421

175122972708

247511582864

2380

806170865021832191

2677

187636

1781

1018

22413053

330

10391248135430281232

4201137

9197941808630

539

1329

226

2676

8272403642

2469

1260

326

2656

11902228

41986

28931031

1834

2325

130

3052

1159393

2709

2818

131115731438

1457

79

311

2545

560

2035

28

2391

1346

1679

1710

347

222317385932143543

7406982445

204

27102933

1058

1167

2064

544

662

257

3017

2295

574

603542995299017941616

2463

789

2249819143

1548771

3182225

11522878

777139514711493

2519

1732

3029191122

1174

2417144

733

2691103025082232

190514465902458

5062318

3034

1990

1140

13031865

298830217491143860180

768

10672454

2202

224017421803

74824142597191429862840844

814163

2669

761

220820978

2136

8562451

2659

1743528600218100

2827

660

17042275

168

251

25032746

2284

982

676

1529

254

2969181723381474

2688

1629

10621809

1100

181313

2322162

599

688337

1640

2812119

2925213482992

5412366

1440

800

1331

283

30914151476

1220

252

2895

19011659

2609

2401

1601

304124711028

929

1951905

2115

832244682926601172729

1739

2122

171161551

1921370

1755

13721937

1110

1483231224042821294

2961

857

838

2511

344

162299

1424

1095222962764

2861

1737140945

174920

1823

24891334

129311259

1166

1183

1837

24123044

1759

926

1177288

1975

230

284903

27381300

5509572181175102997

2810

1065

1612

618

2533

5312806

18182319

1014

28152790

12892722

1135

501

165303017472942

862

1603

2797427

1188

1500

2658

1325

2436

996

28591729

25222718

10362920

14202675

493

1611924

1085

987

214123

1792418

48713642243

2849

1391287624352509

202

2134

2848

2280

668

1180815

2760

2921825

2293

2832

2892958

2355

836

1324

1847

1845

467

28021414474

1744

21525762616

13612479

1154

910

2611

18103018134076328721513

1233

64

2735779

7562799

29141351

2821

2487

98083

1779

940824

310

151

2654

338

2778

766

1430345

2946

114411

17962572

134228712682

2596

1764

788

21711780

1238

331

1567

10892720

1532296319

1534

164728502212

1537

15587972388

2437576

2674

2302

271

29362550

25701741

271316821849772

2149258113321196153830091927

2365

2

234

8671413828

1020283786

2642

28312205

2844

2368

2970

496

1387

2156

27327784611791184

443

500

1027931833250799971

1709

38224

1306

2622

134

2965

33

180728771893

10791770175228002213

637

27

1397

1164

2665

2845

1162

80

15391152125

1032

1211645

2543

2032

287524552517342226235

2496

2703

2927

2314638

26334522884

29510062784

1341

1871

1153

52429632723

2428

521

21701964

1628

2667

1523

2105

2530

1118

29172188

918743

295529532952

13801883164

1609

812

1241

565

132322182937

1574

553

2359

1078

16331181

166710031450

2651

2518

57

104029481392

581333

21873289592306

22551671141018422951291

1768

137653292429810222701423

114

1593

2439

152324114928017501715

1092

44

1139

2169

613

2638

2374

8642392

1123

27742567166305

977

1566

136728831586

8871706

1285

1432

137417581056

2341

2299

1422

2775

9561716

250123501973

1950

189

1798

1763

485

127

23601732966128214662870

2137

29992219

632

1526

18392889

21

75529495371053

1385

20041958

547

1668

109521472780

229

17531580

1663

2238509

206545

1061

2466

51

1145

577

517

2167

258

979

1994

2345

2400

20972116227424922681

2954871

1017

2919

632717

636

2278

2326

1572

2789

1854

1071

2348

1697

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

2743

795

2817

148

27119677

2233023

249529405362211

13216961336

2347

1038

240

396

340

2886

1853

24931161

2390

1681

556

377

1992

1185

782

1793

1998

2846

1081

2866

2689

2144

1403

11912670

1600

2882112010831175

54

244

23522646

1942

190325991495235

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

2378

184

1132

1169

1131

27412142

2542

2423540

2978

2695

1168

2610

205

1766

24742796

1664

2756

1608

916

348

53426351025

2291

8

2582

154

1320

801227

1595

238

1985

1791

841

2615

1338

3722441135

1748

200

774

1986

2413133

2106

2772

272827772630

2874

2666

14121746

2888

2606

111915902971

158

197219448

20125881282632

1023

2976

187

159

559

1125

1703

2773759837

2161

2855

5261582

2120

2353

1564

1170

23812785

2481

43

1967

15851618

520

226011152972

2128

227271

2457

1559212

1578

178779116542114

2541

3042

1429

2132781

129

333

1055

10971604

2692

554

1186297925101944

1565

2164

1264

249

256

2129

2885

2598455

2890

194

2740

1844850

858

2730

2563

2159

2758

1171

531421520

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

463

701301

1128

291837

1614

1113

2385

1091

886

835

2906

18242950

989

1783

1785

1419

2574

1108

1619

107

2107

23571087

2683

741347

25272328

1211852960160

2412783

2371

26051923

325

2256

1596

879

2580136

2343

1701921

2002

7

1782

332

11172648

30642650

21683362133

1568

1088

13982103

16112524

2491

2110

2556

27021571

2569

1584

3072

11111764

110228811106

145

2502

224

1788095252908

15792127

2980

19572

1317

1968

1597

2312354

2637

2303

22791105

1130

17891907

2006

158311262152

1075

21111778

863

2344198

2836

308

2158

2962

2150

1160

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

1610

2625

1786

16782880

2592

2528

1881

2346109615942375

21212657

2693

985

159821312512

170227051141272

1101

11722154

2721

2329

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

26072007

2652

26132641

2367

2351

3075

21462382

2537

2557

934

21512554

1769

3078

1124383322603

2629

1979

264

1997

2101

2198884

2697

1107

350

1605

2671774

23492739

156926473071

1970

225

3060

3066

2719

2552

1988

200121392573

3061

307333424901960

1961

1976

3079

1136

2138

2749

2736

10902538

3065

1122

3069

2766

1962

3059

1592

15331624

2358

306219933068

1717

20081959

1394

1116

30771978

1389

2639

1622

1114

2706

1011

2505

25753076

2680

3074

Main Findings

Future Research

Questions