1# RECORD c0c750e0ff9e1fcc59888eee28b0b7208f5599642# TITLE: Essays on housing markets and economic *growth3# YEAR: 2010.0 VENUE: nan DOI: 10.33915/etd.46524# AUTHORS: Arzu Şen5# PAGES: 126 CANDIDATE TABLE PAGES (1-based): [35, 36, 56, 59, 60, 68, 88, 92, 93, 94, 95, 96, 104, 105]6# ABSTRACT: Essays on Housing Markets and Economic Growth Arzu Sen This dissertation is composed of three essays examining housing markets and economic growth, while carefully considering the role of space. The first chapter serves as an introduction and briefly discusses how each essay contributes to the literature. The second chapter estimates the relationship between residential property values and proximity to coal mines in Monongalia County, West Virginia. The study utilizes a spatial hedonic price model complemented with GIS techniques to estimate the marginal willingness to pay for properties surrounding coal mines. Study findings indicate that proximity to a coal mine translates negatively into property values. The third chapter is related to spatial county growth. Growth theories do not fully specify the prominent factors underlying the data-generating process for growth regressions. Introduction of space in growth regressions further complicates the estimations by adding uncertainty regarding the use of an appropriate spatial weight matrix and spatial regression specification. This study applies Markov Chain Monte Carlo model composition with the Bayesian model averaging methodology on a sample of U.S. counties, to deal with model uncertainty in spatial growth regressions. This study reports model averaged estimates to resolve the uncertainty pertaining to the determinants of U.S. county growth. The fourth chapter is related to the resource curse. An extensive literature has examined the presence and the possible causal mechanisms of a resource curse using a resource dependence indicator. However, a strand of the literature argues that switching from relative measures of resource abundance to absolute measures of resource abundance makes the resource curse disappear across countries. This study contributes to this strand of literature by examining whether coal abundance is a curse or a blessing for county economic growth, using both an absolute and a relative measure of resource abundance. Unlike previous research on resource curse, this study employs spatial county growth regressions to account for spatial dependence. Study findings suggest that introducing spatial dependence into growth regressions changes the results from non-spatial models drastically. When measured as a relative variable, coal dependence has a significant positive direct impact on own-county growth, and positive spillovers on related counties’ growth. When measured as an absolute variab789===== PAGE 1 (score 1, numeric-paren 0, table-words 0, var-words 1) =====1011Graduate Theses, Dissertations, and Problem Reports (ETD) 122010 13Essays on housing markets and economic *growth 14Essays on housing markets and economic *growth 15Arzu Sen 16West Virginia University 17Follow this and additional works at: https://researchrepository.wvu.edu/etd 18Recommended Citation 19Recommended Citation 20Sen, Arzu, "Essays on housing markets and economic *growth" (2010). Graduate Theses, Dissertations, 21and Problem Reports (ETD). 4652. 22https://researchrepository.wvu.edu/etd/4652 23This Dissertation is protected by copyright and/or related rights. It has been brought to you by the The Research 24Repository @ WVU with permission from the rights-holder(s). You are free to use this Dissertation in any way that is 25permitted by the copyright and related rights legislation that applies to your use. For other uses you must obtain 26permission from the rights-holder(s) directly, unless additional rights are indicated by a Creative Commons license 27in the record and/ or on the work itself. This Dissertation has been accepted for inclusion in WVU Graduate Theses, 28Dissertations, and Problem Reports (ETD) collection by an authorized administrator of The Research Repository @ 29WVU. For more information, please contact researchrepository@mail.wvu.edu. 303132===== PAGE 35 (score 147, numeric-paren 114, table-words 1, var-words 15) =====3334Table 2.4: Direct, Indirect, and Total Effects Estimates35Variable36Direct Effects37Indirect 38Effects39Total Effects40Coal Dummy_0.7541-0.154***42 (0.00)43 -0.008***44 (0.00) 45-0.162***46(0.00)47Coal Dummy_0.75~1.0048 -0.105***49(0.00)50 -0.005**51 (0.02) 52-0.110**53(0.02)54Coal Dummy_1.00~1.2555 - 0.093**56 (0.01) 57 -0.005**58 (0.03)59-0.099**60(0.01)61Coal Dummy_1.25~1.5062-0.02063(0.57)64-0.00165(0.62)66-0.02167(0.57)68Building size690.214***70(0.00)710.011*72(0.06)730.225***74(0.00)75Building size squared76-0.020***77(0.00)78-0.001*79(0.09)80-0.021***81(0.00)82Lot size 830.004**84(0.03)850.00086(0.17)870.004**88(0.03)89Lot size squared90-0.00091(0.18)92-0.00093(0.30)94-0.00095(0.18)96Age97-0.10598(0.16)99-0.005100(0.30)101-0.110102(0.17)103Age squared1040.028105(0.67)1060.001107(0.71)1080.075109(0.36)110Excellent Condition1110.459***112(0.00)1130.023*114(0.06)1150.482***116(0.00)117Good Condition1180.337***119(0.00)1200.017*121(0.08)1220.355***123(0.00)124Average Condition1250.249***126(0.00)1270.013128(0.11)1290.262***130(0.00)131Fair Condition132-0.026133(0.75)134-0.001135(0.78)136-0.027137(0.75)138Bedrooms1390.010140(0.30)1410.000142(0.39)1430.010144(0.30)145Bathrooms146 0.029**147 (0.01)1480.001149(0.14)150 0.030**151(0.01)152Heating1530.044154(0.33)1550.002156(0.43)1570.046158(0.33)159Fireplace1600.028*161(0.06)1620.001163(0.19)1640.028*165(0.06)166Full Basement167-0.010168(0.62)169-0.000170(0.66)171-0.011172(0.62)173Part Basement174-0.004175(0.86)1760.000177(0.88)178-0.004179(0.86)180No Basement1810.049*182(0.06)1830.003184(0.20)1850.052*186(0.06)187Urbanized Area Dummy 1880.058***189(0.00)1900.003191(0.10)1920.061***193(0.00)194Lake Dummy1950.102***196(0.00)1970.020***198(0.00)1990.122***200(0.00)201Median Income202-0.004203(0.54)204-0.000205(0.60)206-0.005207(0.54)208White2090.074 210(0.62)2110.004212(0.66)2130.078214(0.62)215College Graduates216-0.030217(0.66)218-0.001219(0.72)220-0.032221(0.66)22227223224225===== PAGE 36 (score 20, numeric-paren 5, table-words 3, var-words 8) =====226227Poverty228-0.036229(0.56)230-0.002231(0.63)232-0.036233(0.56)234R-squared235 0.50236ρ237 0.21***238 (0.00)239Table Notes: Numbers in parentheses are p-values based on heteroskedasticity-consistent standard errors, 240and ***, **, and * represent statistical significance at the 99%, 95%, and 90% level, respectively.241The variables of focus in this study are the coal mine buffer variables. The model 242predicts that a negative buffer variable estimate is associated with disamenity 243characteristics of close proximity to coal mines since the parameter implies lower house 244prices at the mean, all else constant. On the other hand, a positive estimate points to 245amenity impacts of coal mines. The results support the disamenity impacts of coal mines, 246even after controlling for neighborhood variables. The direct effects estimates show the 247average percentage increase in house sales prices resulting from a one unit change in 248various house-specific explanatory variable characteristics. In particular, the direct effect 249of being located within ¾ mile of a coal mine is estimated as -0.15, implying that at the 250mean, being located close to a coal mine reduces own-house price by 15 percent, all else 251constant. Purchasing a house located farther from a coal mine is associated with a lower 252discount. All else constant, houses located within ¾-1 mile and 1-1¼ miles of a coal mine 253experience 11 percent and 10 percent lower average own-house prices respectively. 254Beyond 1¼ miles, the proximity effects are no longer significant. 255Among the variables found to be significant in influencing house prices are house 256condition variables, building size, lot size, number of bathrooms, the presence of a 257fireplace, the urbanized area, and the lake dummies. In Table 4, the direct effect 258magnitude of 0.46 for the excellent house condition implies that the mean house sales 259price is 46 percent higher compared with a house in poor condition, all else constant. 26028261262263===== PAGE 37 (score 11, numeric-paren 0, table-words 1, var-words 9) =====264265Building size is another important factor influencing house prices. A direct effect 266magnitude of 0.21 implies that a thousand square feet increase in house size results in 21 267percent increase in average house sales prices. Type of basement, number of bedrooms, 268the presence of a heating system, and neighborhood variables fail to explain the variation 269in house prices. 270Indirect effects estimates presented in the above table represent spatial spillovers 271and capture neighboring-house effects associated with changes in own-house level 272characteristics. LeSage and Pace (2009) explain that indirect effects estimates can be 273interpreted in two different ways. The first interpretation is named as the average total 274impact on an observation. This interpretation captures how a change in the own-275explanatory variable characteristics of all houses by some constant amount would change 276the price of a typical house. The second interpretation is named as the average total 277impact from an observation, which captures the cumulative impact of a change in own-278house explanatory variable characteristics averaged over all other houses.279Positive indirect effect estimates are indicative of positive spatial externalities, 280whereas negative estimates can be considered as negative spatial externalities. Using the 281second interpretation, the indirect impact magnitude of -0.008 points to negative spillover 282effects of being located within ¾ mile of a coal mine on neighboring-house prices. The 283impact estimates for spillover effects associated with houses near coal mines disappear 284beyond 1¼ miles. House condition variables and building size are significant sources of 285positive spillovers. For instance, compared with a house in poor condition, an excellent 286condition of own-house increases the sales price of neighboring houses by 2 percent. 28729288289290===== PAGE 56 (score 17, numeric-paren 0, table-words 2, var-words 8) =====291292Table 3.3: Descriptive Statistics293Variable294Mean295Std. Dev.296Min297Max298Dependent Variable29915,075.303003,223.563015,910.9530245,621.42303Initial Income 30412,638.883052,884.863063,793.3130730,290.42308Agriculture, Forestry, Fisheries and 309Mining Employment 3101,272.823112,202.38312631364,060314Construction Emp. 3151,814.403165,531.723171318154,612319Manufacturing Emp. 3207,040.1332126,224.563221323884,139324Transportation, Communication and 325other Public Utilities Emp. 3262,234.803278,447.253282329248,416330Wholesale and Retail Emp. 3316,328.9833222,380.663332334700,108335Finance, Insurance and Real Estate Emp. 3361,866.133378,344.953381339249,271340Business and Repair Services Emp. 3411,285.493425,925.643431344203,265345Personal, Entertainment and 346Recreational Services Emp. 3471,274.453485,208.803491350195,217351Health Services Emp. 3522,296.693538,238.123541355250,413356Educational Services Emp. 3572,655.393588,274.173593360250,293361Other Professional and Related Services 362Emp. 3631,312.123645,471.203651366170,887367Federal Government Emp. 3681,114.113694,047.05370137191,087372State Government Emp. 3731,406.193743,763.21375137682,658377Local Government Emp. 3782,680.503799,387.0538012381297,074382Self Employment 3832,139.833846,189.0938511386235,754387Poverty 3888,862.83389 31,421.43906 39113921,003,390393Human Capital 39411,558.5739545,110.563963.913971,383,289398Population Density 399182.264001,562.404010.1440262,674.59403Area 404980.754051,320.9140622.7940720,174.72408NAIX4090.054102.30411-6.441211.17413College town Dummy4140.024150.1334144160417141848419420421===== PAGE 57 (score 7, numeric-paren 0, table-words 0, var-words 7) =====422423 It is commonly acknowledged that human capital is a key determinant of 424economic growth. Following LeSage and Fischer (2008), human capital is measured by 425the skill of the workforce as measured by the log of the number of people with a college 426degree. Following Fingleton (2001), logged population density as well as logged area is 427used. Regions with higher population density would capture agglomeration effects on 428economic growth. Moreover, a poverty variable, which is the logged population below 429poverty line is included. 430As another control, a Natural Amenity Index (NAIX) created by McGranahan 431(1999) is employed. NAIX is calculated from standardized mean values of climate 432measures (January temperature, January days of sun, July temperature and July 433humidity), topographic variation and water area as a proportion of county area (see: 434http://www.ers.usda.gov/Data/NaturalAmenities). A higher NAIX score implies higher 435amenities. 436 Following Higgins et al. (2006), a college town dummy variable is created to 437control for higher incomes in counties which may result from holding a considerable 438amount of advanced degree holders. For all colleges and universities with a total 439enrollment of at least 10,000 students, the ratio of number of students enrolled to 440county’s 1980 population is calculated. The county’s dummy is assigned a value of 1 if 441its enrollment to population ratio is at least 0.2 and a value of 0 otherwise. 442Finally, this study does not incorporate state dummies. Inclusion of state dummies 443results with many of the explanatory variables representing dummy variables, producing 444an invertability problem14. 44514 See LeSage (1999) for details on invertability problem arising from dummy variables. 44649447448449===== PAGE 59 (score 132, numeric-paren 106, table-words 2, var-words 9) =====450451Table 3.4: Direct, Indirect and Total Effects Estimates with Contiguity Weight 452Matrix453Variables454Direct Effects455Indirect Effects456Total Effects457Initial Income458 0.5874***459(0.00)460-0.0197461(0.63)462 0.5677***463(0.00)464Agriculture, Forestry, 465Fisheries, Mining Emp.466 -0.0133***467(0.00)468 -0.0227**469(0.01)470 -0.0360***471(0.00)472Construction Emp.4730.0045474(0.37)475-0.0217476(0.11)477-0.0172478(0.23)479Manufacturing Emp.480-0.0049481(0.10)4820.0032483(0.64)484-0.0017485(0.80)486Transportation, 487Communication and other 488Public Utilities Emp.4890.0040490(0.33)491 -0.0363**492(0.01)493 -0.0322**494(0.03)495Wholesale and Retail Trade 496Emp.4970.0019498(0.82)499 0.0591*500(0.05)501 0.0609*502(0.08)503Finance, Insurance and Real 504Estate Emp.505 0.0248***506(0.00)5070.0165508(0.34)509 0.0413**510(0.03)511Business and Repair 512Services Emp.513 0.0145***514(0.00)5150.0057516(0.69)5170.0201518(0.20)519Personal, Entertainment and 520Recreation Services Emp.521 0.0095**522(0.03)5230.0181524(0.22)525 0.0276*526(0.09)527Health Services Emp.528 0.0098**529(0.01)5300.0164531(0.17)532 0.0262*533(0.05)534Educational Services Emp.535 -0.0339***536(0.00)5370.0431538(0.10)539 0.0091*540(0.09)541Other Professional and 542Related Services Emp.5430.0052544(0.20)545-0.0030546(0.84)5470.0022548(0.90)549Federal Government 550Employment5510.0025552(0.33)553 0.0211***554(0.00)555 0.0236***556(0.00)557State Government 558Employment559 -0.0131***560(0.00)5610.0058562(0.51)563 -0.0073564(0.43)565Local Government 566Employment567 -0.0141**568(0.04)5690.0004570(0.98)571 -0.0137572(0.53)573Self Employment574 0.0803***575(0.00)576-0.0063577(0.64)578 0.0740***579(0.00)580Poverty581 -0.0256***582(0.00)5830.0144584(0.20)585 -0.0112586(0.15)587Human Capital588 0.1133***589(0.00)590 -0.0397*591(0.08)592 0.0736***593(0.00)594College town Dummy595 -0.0194***596(0.00)597 -0.0652***598(0.00)599 -0.0846***600(0.00)601Population Density602 -0.1850***603(0.00)604-0.0481605(0.18)606 -0.2331***607(0.00)608Area609 -0.1967***610(0.00)611 -0.0823**612(0.03)613 -0.2788***614(0.00)615NAIX6160.0002617(0.60)618 -0.0021**619(0.03)620 -0.0018**621(0.03)622R-squared6230.8255624ρ6250.5315***626(0.00)627Ambiguous estimates across different specifications are highlighted with bold fonts. 628p-levels are reported in parenthesis. 629***Significant at the 99% level630**Significant at the 95% level631*Significant at the 90% level63251633634635===== PAGE 60 (score 125, numeric-paren 99, table-words 2, var-words 9) =====636637Table 3.5: Direct, Indirect and Total Effects Estimates with 14 Nearest Neighbors638Variables639Direct Effects640Indirect Effects641Total Effects642Initial Income643 0.5920***644(0.00)645-0.0105646(0.88)647 0.5815***648(0.00)649Agriculture, Forestry, 650Fisheries, Mining Emp.651 -0.0131***652(0.00)653-0.0219654(0.10)655 -0.0350***656(0.00)657Construction Emp.6580.0057659(0.24)660 -0.0084661(0.70)662-0.0028663(0.90)664Manufacturing Emp.665 -0.0043666(0.13)6670.0093668(0.39)6690.0049670(0.65)671Transportation, 672Communication and other 673Public Utilities Emp.6740.0058675(0.16)676 0.0429*677(0.08)678-0.0371679(0.15)680Wholesale and Retail Trade 681Emp.6820.0053683(0.52)6840.0798685(0.19)6860.0746687(0.24)688Finance, Insurance and Real 689Estate Emp.690 0.0256***691(0.00)692 0.0593*693(0.09)694 0.0850**695(0.02)696Business and Repair 697Services Emp.698 0.0199***699(0.00)700-0.0364701(0.19)702-0.0165703(0.57)704Personal, Entertainment and 705Recreation Services Emp.7060.0052707(0.21)7080.0204709(0.45)7100.0256711(0.36)712Health Services Emp.713 0.0090**714(0.01)7150.0213716(0.35)7170.0303718(0.20)719Educational Services Emp.720 -0.0347***721(0.00)722 0.1015**723(0.04)724 0.0668*725(0.06)726Other Professional and 727Related Services Emp.7280.0032729(0.44)730 -0.0071731(0.81)732 -0.0039733(0.90)734Federal Government 735Employment7360.0020737(0.42)738 0.0397***739(0.00)740 0.0416***741(0.00)742State Government 743Employment744 -0.0143***745(0.00)7460.0025747(0.87)748-0.0118749(0.46)750Local Government 751Employment752 -0.0134*753(0.05)754-0.0329755(0.36)756-0.0462757(0.21)758Self Employment759 0.0798***760(0.00)761-0.0077762(0.73)763 0.0722***764(0.00)765Poverty766 -0.0280***767(0.00)7680.0165769(0.21)770-0.0115771(0.31)772Human Capital773 0.1142***774(0.00)775 -0.0707*776(0.07)7770.0434778(0.28)779College town Dummy780 -0.0181***781(0.00)782 -0.1443***783(0.00)784 -0.1623***785(0.00)786Population Density787 -0.1771***788(0.00)789-0.0920790(0.14)791 -0.2691***792(0.00)793Area794 -0.1904***795(0.00)796 -0.1194*797(0.07)798 -0.3099***799(0.00)800NAIX8010.0006802(0.20)803 -0.0028*804(0.05)805-0.0022806(0.11)807R-squared8080.8185809ρ8100.6517***811(0.00)812Ambiguous estimates across different specifications are highlighted with bold fonts. 813p-levels are reported in parenthesis. 814***Significant at the 99% level815**Significant at the 95% level816*Significant at the 90% level81752818819820===== PAGE 61 (score 11, numeric-paren 0, table-words 0, var-words 11) =====821822In both model specifications, own-county employment in the agricultural 823industries and educational services are negatively related to own-county growth. Own-824county employment in finance, insurance and real estate, business and repair services, 825and health services have a positive impact on own-county growth. One ambiguity arising 826from different weight matrix specifications pertains to estimates of employment in 827wholesale and retail trade and entertainment and recreational services. Both estimates 828from the model with contiguity weight matrix specification are significant at 90 percent 829level, whereas estimates from the other model specification are not significant. 830Across two model specifications, state and local government employment in own-831county has a significant negative impact on own-county growth, suggesting an 832overexpansion of the state government. Interestingly, federal government in own-county 833does not hinder own-county growth. Self employment and human capital have the 834expected positive impacts on own-county growth, with relatively large magnitudes. 835Poverty is associated with lower income levels. Population density, which captures the 836agglomeration effects, has a strong negative impact on own-county growth. This result 837reflects the congestion effects and disamenities connected to large cities and denser areas. 838Another result of interest is that college towns are associated with lower income levels, 839capturing the impact of disproportionate number of students residing in college towns. 840Indirect effects estimates, presented in the above tables, represent spatial 841spillovers and capture related-county effects associated with changes in own-county level 842characteristics. LeSage and Pace (2009) explain that indirect effects estimates can be 843interpreted in two different ways. The first interpretation is named as the average total 844impact on an observation. This interpretation captures how a change in the initial level of 84553846847848===== PAGE 68 (score 122, numeric-paren 98, table-words 1, var-words 9) =====849850Table 3.7: Model Averaged Estimates851Variables852Direct Effects853Indirect Effects854Total Effects855Initial Income856 0.6199***857(0.00)858-0.0212859(0.81)860 0.5987***861(0.00)862Agriculture, Forestry, 863Fisheries, Mining Emp.864 -0.0153***865(0.00)866-0.0188867(0.13)868 -0.0341***869(0.00)870Construction Emp.8710.0083872(0.39)873-0.0132874(0.57)875-0.0049876(0.87)877Manufacturing Emp.878 -0.0074**879(0.03)8800.0053881(0.41)882 -0.0021**883(0.04)884Transportation, 885Communication and other 886Public Utilities Emp.8870.0045888(0.30)889-0.0699890(0.15)891-0.0654892(0.17)893Wholesale and Retail Trade 894Emp.8950.0025896(0.80)897 0.0472**898(0.03)899 0.0497*900(0.05)901Finance, Insurance and Real 902Estate Emp.903 0.0261***904(0.00)9050.0324906(0.29)907 0.0585**908(0.03)909Business and Repair 910Services Emp.911 0.0156***912(0.00)9130.0087914(0.58)9150.0243916(0.68)917Personal, Entertainment and 918Recreation Services Emp.9190.0066920(0.16)9210.0231922(0.50)9230.0297924(0.40)925Health Services Emp.9260.0104927(0.14)9280.0267929(0.42)9300.0371931(0.25)932Educational Services Emp.933 -0.0419***934(0.00)935 0.1172**936(0.02)937 0.0753**938(0.04)939Other Professional and 940Related Services Emp.9410.0045942(0.38)943-0.0028944(0.83)9450.0017946(0.88)947Federal Government 948Employment9490.0031950(0.42)951 0.0489***952(0.00)9530.0520954(0.10)955State Government 956Employment957 -0.0194***958(0.00)9590.0065960(0.49)961-0.0129962(0.45)963Local Government 964Employment965-0.0150966(0.11)967-0.0359968(0.39)969-0.0509970(0.20)971Self Employment972 0.0962***973(0.00)974-0.0051975(0.52)976 0.0911***977(0.00)978Poverty979 -0.0324***980(0.00)9810.0179982(0.21)983-0.0145984(0.26)985Human Capital986 0.1197***987(0.00)988 -0.0764**989(0.01)990 0.0433*** 991(0.00)992College town Dummy993 -0.0172***994(0.00)995 -0.0776***996(0.00)997 -0.0948***998(0.00)999Population Density1000 -0.1902***1001(0.00)1002 -0.1069*1003(0.07)1004 -0.2971***1005(0.00)1006Area1007 -0.1994***1008(0.00)1009 -0.0941**1010(0.03)1011 -0.2935***1012(0.00)1013NAIX10140.00021015(0.61)1016 -0.0033*1017(0.08)1018-0.00311019(0.13)1020p-levels are reported in parenthesis. 1021***Significant at the 99% level1022**Significant at the 95% level1023*Significant at the 90% level102460102510261027===== PAGE 69 (score 5, numeric-paren 0, table-words 0, var-words 5) =====102810293.5 Conclusion1030Spatial growth regression models produce estimates and inferences that are conditional 1031on both the particular weight matrix used to specify the spatial dependence and the 1032explanatory variables included in the growth regression. Employing different spatial 1033weight matrices often lead to ambiguous results in spatial econometrics. This study 1034applies Monte Carlo Markov Chain model composition MC3 with Bayesian model 1035averaging on a sample of U.S. counties to deal with model uncertainty in spatial 1036regressions and produce model averaged estimates and inferences. The study first 1037displays the results based on two spatial weight matrices, contiguity and optimal nearest 1038neighbors, and discusses the ambiguity resulting from different weight matrix 1039specifications. Later, model averaged estimates are reported to resolve the uncertainty 1040pertaining to the determinants of U.S. county growth. 1041 1042The Bayesian solution to model uncertainty clarifies the ambiguous estimates 1043across different specifications. For instance, the model averaged estimates reveal that 1044human capital imposes a positive direct impact and negative spillover effects on related 1045counties, and it has a positively significant total impact. The methodology also proves 1046fruitful as it clarifies the ambiguity pertaining to the significance of employment in 1047agricultural services, wholesale and retail trade, transportation and communication 1048services, finance and real estate, entertainment and recreational services, health services, 1049and natural amenities index. 105061105110521053===== PAGE 88 (score 18, numeric-paren 0, table-words 2, var-words 8) =====10541055Table 4.3: Descriptive Statistics1056Variable1057Mean1058Std. Dev.1059Min1060Max1061Dependent Variable106215,075.3010633,223.5610645,910.95106545,621.421066Coal production per capita in USD 1067346.1710683,163.68106911070104,192.601071Coal production per income in USD 10720.0310730.2510740107510.111076Initial Income 107712,638.8810782,884.8610793,793.31108030,290.421081Agriculture, Forestry, Fisheries and 1082Mining Employment 10831,272.8210842,202.3810856108664,0601087Construction Emp. 10881,814.4010895,531.72109011091154,6121092Manufacturing Emp. 10937,040.13109426,224.56109511096884,1391097Transportation, Communication and 1098other Public Utilities Emp. 10992,234.8011008,447.25110121102248,4161103Wholesale and Retail Emp. 11046,328.98110522,380.66110621107700,1081108Finance, Insurance and Real Estate Emp. 11091,866.1311108,344.95111111112249,2711113Business and Repair Services Emp. 11141,285.4911155,925.64111611117203,2651118Personal, Entertainment and 1119Recreational Services Emp. 11201,274.4511215,208.80112211123195,2171124Health Services Emp. 11252,296.6911268,238.12112711128250,4131129Educational Services Emp. 11302,655.3911318,274.17113231133250,2931134Other Professional and Related Services 1135Emp. 11361,312.1211375,471.20113811139170,8871140Federal Government Emp. 11411,114.1111424,047.0511431114491,0871145State Government Emp. 11461,406.1911473,763.2111481114982,6581150Local Government Emp. 11512,680.5011529,387.051153121154297,0741155Self Employment 11562,139.8311576,189.091158111159235,7541160Poverty 11618,862.83116231,421.461163111641,003,3901165Human Capital 116611,558.57116745,110.5611683.9111691,383,2891170Population Density 1171182.2611721,562.4011730.14117462,674.591175Area 1176980.7511771,320.91117822.79117920,174.721180NAIX11810.0511822.291183-6.4118411.171185College town Dummy11860.0211870.131188011891119080119111921193===== PAGE 89 (score 13, numeric-paren 0, table-words 1, var-words 11) =====11941195The study utilizes a large dataset controlling for as many growth determinants as 1196possible. All variables are measured as initial period values (i.e. 1980 values) to avoid 1197endogeneity problems. Initial period level of income, one common variable included in 1198growth regressions, is included to control for conditional convergence. This study uses 1199per capita personal income data obtained from the Bureau of Economic Analysis (BEA) 1200and expressed in 1990 U.S. dollars, using the CPI Research Series deflator. BEA income 1201data is collected according to place of work and adjusted for place of residence, to make 1202the income data compatible with Census data. The dependent variable is the income level 1203rather than the growth rate, measured by the log of average per capita income for the 1204period 1980-1999. 1205This study controls for industry mix by incorporating 11 employment-by-industry 1206variables21. The logged levels of employment are used, following Lopez-Bazo et al. 1207(2004) and LeSage and Fischer (2008). Following Higgins et al. (2006), the size of the 1208public sector is controlled by employing the logged levels of employment in federal, state 1209and local governments. Additionally, logged levels of self employed are included to 1210obtain its marginal effect. 1211The economic growth literature has often identified human capital as an important 1212growth determinant. This study follows LeSage and Fischer (2008) and measures human 1213capital by the log of the number of people with a college degree. Following Fingleton 1214(2001), logged population density as well as logged area is used. Agglomeration effects 1215on economic growth may be captured by regions with higher population density. 121621 The industry groups are agriculture, forestry, fisheries and mining; construction; manufacturing of 1217durables and nondurables; transportation, communication and other public utilities; wholesale and retail; 1218finance, insurance and real estate; business and repair services; personal, entertainment and recreation 1219services; health services; educational services, other professional and related services.122081122112221223===== PAGE 92 (score 135, numeric-paren 108, table-words 2, var-words 9) =====12241225Table 4.4: Direct, Indirect and Total Effects Estimates with Contiguity Weight 1226Matrix and an Absolute Measure of Coal Abundance1227Variable1228Direct Effects1229Indirect Effects1230Total Effects1231Coal Production per Capita 1232($) 1233 0.00011234(0.88)1235-0.00151236(0.43)1237-0.00141238(0.46)1239Initial Income1240 0.5882***1241(0.00)1242-0.01651243(0.69)1244 0.5717***1245(0.00)1246Agriculture, Forestry, 1247Fisheries, Mining Emp.1248 -0.0133***1249(0.00)1250 -0.0212**1251(0.01)1252 -0.0345***1253(0.00)1254Construction Emp.12550.00451256(0.36)1257-0.02221258(0.11)1259-0.01771260(0.22)1261Manufacturing Emp.1262 -0.0051*1263(0.09)12640.00241265(0.74)1266-0.00271267(0.10)1268Transportation, 1269Communication and other 1270Public Utilities Emp.12710.00431272(0.31)1273 -0.0336**1274(0.02)1275 -0.0293*1276(0.05)1277Wholesale and Retail Trade 1278Emp.12790.00151280(0.86)1281 0.0564*1282(0.07)1283 0.0579*1284(0.09)1285Finance, Insurance and Real 1286Estate Emp.1287 0.0244***1288(0.00)12890.01481290(0.40)1291 0.0392**1292(0.04)1293Business and Repair 1294Services Emp.1295 0.0145***1296(0.00)12970.00401298(0.78)12990.01851300(0.23)1301Personal, Entertainment and 1302Recreation Services Emp.1303 0.0096**1304(0.02)13050.01831306(0.20)1307 0.0279*1308(0.07)1309Health Services Emp.1310 0.0098**1311(0.01)13120.01721313(0.16)1314 0.0270**1315(0.04)1316Educational Services Emp.1317 -0.0339***1318(0.00)13190.04251320(0.11)13210.00861322(0.77)1323Other Professional and 1324Related Services Emp.13250.00531326(0.20)1327-0.00261328(0.86)13290.00271330(0.87)1331Federal Government 1332Employment13330.00251334(0.33)1335 0.0207***1336(0.00)1337 0.0232***1338(0.00)1339State Government 1340Employment1341 -0.0130***1342(0.00)13430.00561344(0.51)1345-0.00741346(0.41)1347Local Government 1348Employment1349 -0.0144**1350(0.04)1351-0.00301352(0.88)1353-0.01741354(0.42)1355Self Employment1356 0.0802***1357(0.00)1358-0.00801359(0.56)1360 0.0723***1361(0.00)1362Poverty1363 -0.0255***1364(0.00)13650.01411366(0.21)1367-0.01141368(0.14)1369Human Capital1370 0.1132***1371(0.00)1372-0.03401373(0.08)1374 0.0732***1375(0.00)1376College town Dummy1377 -0.0193***1378(0.00)1379 -0.0638***1380(0.00)1381 -0.0831***1382(0.00)1383Population Density1384 -0.1839***1385(0.00)1386-0.03941387(0.29)1388 -0.2233***1389(0.00)1390Area1391 -0.1955***1392(0.00)1393 -0.0742*1394(0.06)1395 -0.2697***1396(0.00)1397NAIX13980.00031399(0.59)1400 -0.0020**1401(0.03)1402 -0.0017**1403(0.04)1404R-squared14050.82571406ρ14070.5297***1408(0.0000)1409p-levels are reported in parenthesis. 1410***Significant at the 99% level1411**Significant at the 95% level1412*Significant at the 90% level141384141414151416===== PAGE 93 (score 139, numeric-paren 112, table-words 2, var-words 9) =====14171418Table 4.5: Direct, Indirect and Total Effects Estimates with Contiguity Weight 1419Matrix and a Relative Measure of Coal Dependence1420Variable1421Direct Effects1422Indirect Effects1423Total Effects1424Coal Production per Income 1425($)1426 0.0079***1427(0.00)14280.00811429(0.50)14300.01611431(0.22)1432Initial Income1433 0.5849***1434(0.00)1435-0.02241436(0.57)1437 0.5625***1438(0.00)1439Agriculture, Forestry, 1440Fisheries, Mining Emp.1441 -0.0142***1442(0.00)1443 -0.0238***1444(0.00)1445 -0.0379***1446(0.00)1447Construction Emp.14480.00401449(0.41)1450 -0.0239*1451(0.08)1452-0.01991453(0.16)1454Manufacturing Emp.1455-0.00341456(0.27)14570.00421458(0.55)1459 0.0008*1460(0.09)1461Transportation, 1462Communication and other 1463Public Utilities Emp.14640.00331465(0.42)1466 -0.0371**1467(0.01)1468 -0.0338**1469(0.03)1470Wholesale and Retail Trade 1471Emp.14720.00421473(0.62)1474 0.0621**1475(0.04)1476 0.0662*1477(0.05)1478Finance, Insurance and Real 1479Estate Emp.1480 0.0251***1481(0.00)14820.01701483(0.32)1484 0.0421**1485(0.02)1486Business and Repair 1487Services Emp.1488 0.0154***1489(0.00)14900.00871491(0.53)14920.02411493(0.12)1494Personal, Entertainment and 1495Recreation Services Emp.1496 0.0092**1497(0.03)14980.01861499(0.20)1500 0.0278*1501(0.08)1502Health Services Emp.1503 0.0100***1504(0.00)15050.01631506(0.18)1507 0.0263*1508(0.05)1509Educational Services Emp.1510 -0.0348***1511(0.00)15120.04191513(0.10)15140.00711515(0.80)1516Other Professional and 1517Related Services Emp.1518 0.0050*1519(0.07)1520-0.00411521(0.79)15220.00091523(0.96)1524Federal Government 1525Employment15260.00321527(0.21)1528 0.0207***1529(0.00)1530 0.0239***1531(0.00)1532State Government 1533Employment1534 -0.0123***1535(0.00)15360.00631537(0.46)1538-0.00601539(0.50)1540Local Government 1541Employment1542 -0.0131*1543(0.09)15440.00381545(0.86)1546-0.00931547(0.67)1548Self Employment1549 0.0815***1550(0.00)1551-0.00511552(0.70)1553 0.0764***1554(0.00)1555Poverty1556 -0.0258***1557(0.00)15580.01461559(0.20)1560-0.01111561(0.14)1562Human Capital1563 0.1139***1564(0.00)1565 -0.0398*1566(0.07)1567 0.0741***1568(0.00)1569College town Dummy1570 -0.0196***1571(0.00)1572 -0.0657***1573(0.00)1574 -0.0853***1575(0.00)1576Population Density1577 -0.1916***1578(0.00)1579-0.05511580(0.14)1581 -0.2468***1582(0.00)1583Area1584 -0.2027***1585(0.00)1586 -0.0889**1587(0.02)1588 -0.2916***1589(0.00)1590NAIX15910.00031592(0.53)1593 -0.0021**1594(0.03)1595 -0.0018**1596(0.03)1597R-squared15980.82581599ρ16000.5300***1601(0.0000)1602p-levels are reported in parenthesis. 1603***Significant at the 99% level1604**Significant at the 95% level1605*Significant at the 90% level160685160716081609===== PAGE 94 (score 127, numeric-paren 100, table-words 2, var-words 9) =====16101611Table 4.6: Direct, Indirect and Total Effects Estimates with 14 Nearest Neighbors 1612and an Absolute Measure of Coal Abundance1613Variable1614Direct Effects1615Indirect Effects1616Total Effects1617Coal Production per Capita 1618($)1619-0.00011620(0.92)1621-0.00221622(0.50)1623-0.00221624(0.48)1625Initial Income1626 0.5917***1627(0.00)1628-0.00551629(0.93)1630 0.5862***1631(0.00)1632Agriculture, Forestry, 1633Fisheries, Mining Emp.1634 -0.0127***1635(0.00)1636-0.01951637(0.15)1638 -0.0322**1639(0.02)1640Construction Emp.16410.00561642(0.25)1643-0.00851644(0.70)1645-0.00301646(0.90)1647Manufacturing Emp.1648 -0.0044**1649(0.03)16500.00731651(0.50)1652 0.0029*1653(0.09)1654Transportation, 1655Communication and other 1656Public Utilities Emp.16570.00621658(0.13)1659-0.03851660(0.13)1661-0.03221662(0.22)1663Wholesale and Retail Trade 1664Emp.1665-0.00481666(0.55)16670.07861668(0.20)16690.07371670(0.25)1671Finance, Insurance and Real 1672Estate Emp.1673 0.0254***1674(0.00)16750.05651676(0.10)1677 0.0819**1678(0.02)1679Business and Repair 1680Services Emp.1681 0.0196***1682(0.00)1683-0.04031684(0.15)1685-0.02071686(0.47)1687Personal, Entertainment and 1688Recreation Services Emp.16890.00531690(0.21)16910.01971692(0.46)16930.02501694(0.37)1695Health Services Emp.1696 0.0093**1697(0.01)16980.02411699(0.28)17000.03341701(0.15)1702Educational Services Emp.1703 -0.0345***1704(0.00)1705 0.1054**1706(0.03)17070.07081708(0.17)1709Other Professional and 1710Related Services Emp.17110.00321712(0.43)1713-0.00701714(0.81)1715-0.00381716(0.90)1717Federal Government 1718Employment17190.00201720(0.42)1721 0.0396***1722(0.00)1723 0.0416***1724(0.00)1725State Government 1726Employment1727 -0.0146***1728(0.00)17290.00091730(0.95)1731-0.01361732(0.41)1733Local Government 1734Employment1735 -0.0133*1736(0.08)1737-0.03901738(0.29)1739-0.05221740(0.16)1741Self Employment1742 0.0794***1743(0.00)1744-0.01151745(0.61)1746 0.0678***1747(0.00)1748Poverty1749 -0.0277***1750(0.00)17510.01591752(0.23)1753-0.01181754(0.30)1755Human Capital1756 0.1141***1757(0.00)1758 -0.0730*1759(0.06)17600.04101761(0.30)1762College town Dummy1763 -0.0179***1764(0.00)1765 -0.1428***1766(0.00)1767 -0.1607***1768(0.00)1769Population Density1770 -0.1773***1771(0.00)1772-0.07971773(0.22)1774 -0.2570***1775(0.00)1776Area1777 -0.1906***1778(0.00)1779-0.10931780(0.11)1781 -0.2999***1782(0.00)1783NAIX17840.00071785(0.18)1786 -0.0025*1787(0.08)1788-0.00191789(0.18)1790R-squared17910.81861792ρ17930.6494***1794(0.00)1795p-levels are reported in parenthesis. 1796***Significant at the 99% level1797**Significant at the 95% level1798*Significant at the 90% level179986180018011802===== PAGE 95 (score 135, numeric-paren 108, table-words 2, var-words 9) =====18031804Table 4.7: Direct, Indirect and Total Effects Estimates with 14 Nearest Neighbors 1805and a Relative Measure of Coal Dependence1806Variable1807Direct Effects1808Indirect Effects1809Total Effects1810Coal Production per Income 1811($)1812 0.0077***1813(0.00)1814 0.0452*1815(0.06)1816 0.0529**1817(0.03)1818Initial Income1819 0.5878***1820(0.00)1821-0.02301822(0.73)1823 0.5648***1824(0.00)1825Agriculture, Forestry, 1826Fisheries, Mining Emp.1827 -0.0140***1828(0.00)1829 -0.0254*1830(0.07)1831 -0.0395**1832(0.00)1833Construction Emp.18340.00501835(0.23)1836-0.01301837(0.56)1838-0.00771839(0.73)1840Manufacturing Emp.1841-0.00271842(0.34)18430.01571844(0.15)18450.01291846(0.14)1847Transportation, 1848Communication and other 1849Public Utilities Emp.18500.00501851(0.23)1852 -0.0487*1853(0.05)1854 -0.0437*1855(0.09)1856Wholesale and Retail Trade 1857Emp.1858-0.00341859(0.68)1860 0.1019*1861(0.09)18620.08841863(0.16)1864Finance, Insurance and Real 1865Estate Emp.1866 0.0261***1867(0.00)1868 0.0642*1869(0.06)1870 0.0903**1871(0.01)1872Business and Repair 1873Services Emp.1874 0.0207***1875(0.00)1876-0.02451877(0.37)1878-0.00381879(0.89)1880Personal, Entertainment and 1881Recreation Services Emp.18820.00491883(0.25)18840.02191885(0.42)18860.02681887(0.34)1888Health Services Emp.1889 0.0094**1890(0.01)18910.02131892(0.34)18930.03071894(0.18)1895Educational Services Emp.1896 -0.0357***1897(0.00)1898 0.0933*1899(0.06)19000.05751901(0.27)1902Other Professional and 1903Related Services Emp.19040.00291905(0.47)1906-0.01211907(0.68)1908-0.00931909(0.77)1910Federal Government 1911Employment19120.00261913(0.29)1914 0.0397***1915(0.00)1916 0.0422***1917(0.00)1918State Government 1919Employment1920 -0.0136***1921(0.00)19220.00641923(0.69)1924-0.00711925(0.67)1926Local Government 1927Employment1928-0.01161929(0.10)1930-0.01791931(0.62)1932-0.02951933(0.43)1934Self Employment1935 0.0819***1936(0.00)1937-0.00421938(0.85)1939 0.0777***1940(0.00)1941Poverty1942 -0.0279***1943(0.00)19440.01701945(0.19)1946-0.01091947(0.33)1948Human Capital1949 0.1147***1950(0.00)1951 -0.0683*1952(0.08)19530.04641954(0.25)1955College town Dummy1956 -0.0183***1957(0.00)1958 -0.1466***1959(0.00)1960 -0.1649***1961(0.00)1962Population Density1963 -0.1843***1964(0.00)1965 -0.1297*1966(0.05)1967 -0.3140***1968(0.00)1969Area1970 -0.1974***1971(0.00)1972 -0.1538**1973(0.03)1974 -0.3512***1975(0.00)1976NAIX19770.00071978(0.19)1979 -0.0030**1980(0.03)1981 -0.0023*1982(0.08)1983R-squared19840.81881985ρ19860.6519***1987(0.00)1988p-levels are reported in parenthesis. 1989***Significant at the 99% level1990**Significant at the 95% level1991*Significant at the 90% level199287199319941995===== PAGE 96 (score 108, numeric-paren 86, table-words 2, var-words 9) =====19961997Table 4.8: OLS Results with an Absolute and a Relative Measure of Coal 1998Abundance1999Variable2000OLS(1)2001OLS(2)2002Coal Production per Capita 2003($) 2004 0.0020**2005(0.01)2006-2007Coal Production per Income 2008($) 2009-2010 -0.0036**2011(0.02)2012Initial Income2013 0.4387***2014(0.00)2015 0.4369***2016(0.00)2017Agriculture, Forestry, 2018Fisheries, Mining Emp.2019 -0.0180***2020(0.00)2021 -0.0203***2022(0.00)2023Construction Emp.2024 -0.0147**2025(0.01)2026 -0.0155***2027(0.00)2028Manufacturing Emp.2029 -0.0104***2030(0.00)2031 -0.0084***2032(0.00)2033Transportation, 2034Communication and other 2035Public Utilities Emp.2036-0.00332037(0.51)2038-0.00492039(0.33)2040Wholesale and Retail Trade 2041Emp.2042 -0.0226**2043(0.02)2044 -0.0202**2045(0.04)2046Finance, Insurance and Real 2047Estate Emp.2048 0.0351***2049(0.00)2050 0.0360***2051(0.00)2052Business and Repair 2053Services Emp.2054 0.0113**2055(0.01)2056 0.0127**2057(0.01)2058Personal, Entertainment and 2059Recreation Services Emp.2060 0.0258***2061(0.00)2062 0.0259***2063(0.00)2064Health Services Emp.2065 -0.0076*2066(0.06)2067 -0.0078*2068(0.05)2069Educational Services Emp.2070 -0.0277***2071(0.00)2072 -0.0291***2073(0.00)2074Other Professional and 2075Related Services Emp.2076 0.0125**2077(0.01)2078 0.0125**2079(0.01)2080Federal Government 2081Employment2082-0.00082083(0.78)2084-0.00072085(0.81)2086State Government 2087Employment2088 -0.0117***2089(0.00)2090 -0.0112***2091(0.000)2092Local Government 2093Employment2094-0.01012095(0.20)2096-0.01332097(0.10)2098Self Employment2099 0.0849***2100(0.00)2101 0.0884***2102(0.00)2103Poverty2104 -0.0387***2105(0.00)2106 -0.1836***2107(0.00)2108Human Capital2109 0.1100***2110(0.00)2111 0.1124***2112(0.00)2113College town Dummy2114 -0.0299***2115(0.00)2116 -0.0304***2117(0.00)2118Population Density2119 -0.1528***2120(0.00)2121 -0.1644***2122(0.00)2123Area2124 -0.1945***2125(0.00)2126 -0.2053***2127(0.00)2128NAIX2129 -0.0017***2130(0.00)2131 -0.0017***2132(0.00)2133R-squared21340.828721350.82842136p-levels are reported in parenthesis. 2137***Significant at the 99% level2138**Significant at the 95% level2139*Significant at the 90% level214088214121422143===== PAGE 97 (score 7, numeric-paren 0, table-words 2, var-words 3) =====21442145Indirect effects estimates presented in the above tables represent spatial spillovers 2146and capture related-county effects associated with changes in own-county level 2147characteristics. LeSage and Pace (2009) explain that indirect effects estimates can be 2148interpreted in two different ways. The first interpretation is named as the average total 2149impact on an observation. This interpretation captures how a change in the initial level of 2150explanatory variable characteristics of all counties by some constant amount would 2151change the income level of a typical county. The second interpretation is named as the 2152average total impact from an observation, which captures the cumulative impact of a 2153change in own-county initial level of explanatory variable characteristics averaged over 2154all other regions. The counties that are closely related to own-county would experience 2155greater income effects, while the ones that are loosely related would have smaller 2156impacts. 2157Total effect estimates are the sum of direct and indirect estimates. As depicted in 2158Table 4.5, the positive magnitude of total impact of initial income level suggests that 2159higher income levels lead to higher current levels of county income. 2160Direct, indirect and total effects estimates of the absolute coal abundance variable 2161are not significant in either model specification. On the other hand, the direct effects 2162estimates of the relative coal dependence variable are positive and significant in both 2163specifications. These results are in stark contrast to the OLS regression results displayed 2164on Table 4.8. The OLS results suggest a negative significant relationship between the 2165relative coal dependence variable and county growth, and a positive insignificant 2166relationship between the absolute coal abundance variable and county growth. The OLS 2167results are in line with the strand of cross-country growth literature that suggests that 216889216921702171===== PAGE 104 (score 125, numeric-paren 101, table-words 1, var-words 9) =====21722173Table 4.10: Model Averaged Estimates with Absolute Coal Abundance Measure2174Variables2175Direct Effects2176Indirect Effects2177Total Effects2178Coal Production per Capita 2179($)2180-0.00012181(0.91)2182-0.00242183(0.52)2184-0.00252185(0.49)2186Initial Income2187 0.6186***2188(0.00)2189-0.01992190(0.81)2191 0.5987***2192(0.00)2193Agriculture, Forestry, 2194Fisheries, Mining Emp.2195 -0.0151***2196(0.00)2197-0.01912198(0.14)2199 -0.0342***2200(0.00)2201Construction Emp.22020.00782203(0.39)2204-0.01392205(0.56)2206-0.00612207(0.85)2208Manufacturing Emp.2209 -0.0071**2210(0.04)22110.00602212(0.44)2213 -0.0011*2214(0.05)2215Transportation, 2216Communication and other 2217Public Utilities Emp.22180.00522219(0.32)2220-0.06812221(0.14)2222-0.06292223(0.16)2224Wholesale and Retail Trade 2225Emp.22260.00262227(0.80)2228 0.0464**2229(0.03)2230 0.0490**2231(0.04)2232Finance, Insurance and Real 2233Estate Emp.2234 0.0262***2235(0.00)22360.03392237(0.28)2238 0.0601**2239(0.02)2240Business and Repair 2241Services Emp.2242 0.0158***2243(0.00)22440.00842245(0.55)22460.02422247(0.67)2248Personal, Entertainment and 2249Recreation Services Emp.22500.00682251(0.17)22520.02372253(0.50)22540.03052255(0.40)2256Health Services Emp.22570.00982258(0.14)22590.02612260(0.41)22610.03592262(0.25)2263Educational Services Emp.2264 -0.0415***2265(0.00)2266 0.1188**2267(0.02)2268 0.0773**2269(0.04)2270Other Professional and 2271Related Services Emp.22720.00472273(0.38)2274-0.00292275(0.83)22760.00182277(0.87)2278Federal Government 2279Employment22800.00292281(0.31)2282 0.0481**2283(0.01)22840.05102285(0.11)2286State Government 2287Employment2288 -0.0193***2289(0.00)22900.00632291(0.49)2292-0.01302293(0.46)2294Local Government 2295Employment2296-0.01532297(0.11)2298-0.03612299(0.39)2300-0.05142301(0.20)2302Self Employment2303 0.0960***2304(0.00)2305-0.00482306(0.52)2307 0.0912***2308(0.00)2309Poverty2310 -0.0329***2311(0.00)23120.01872313(0.21)2314-0.01422315(0.26)2316Human Capital2317 0.1195***2318(0.00)2319 -0.0768**2320(0.01)2321 0.0427*** 2322(0.00)2323College town Dummy2324 -0.0169***2325(0.00)2326 -0.0762***2327(0.00)2328 -0.0931***2329(0.00)2330Population Density2331 -0.1905***2332(0.00)2333 -0.1071*2334(0.07)2335 -0.2976***2336(0.00)2337Area2338 -0.1990***2339(0.00)2340 -0.0937**2341(0.03)2342 -0.2927***2343(0.00)2344NAIX23450.00022346(0.61)2347 -0.0030*2348(0.09)2349-0.00322350(0.13)2351p-levels are reported in parenthesis. 2352***Significant at the 99% level2353**Significant at the 95% level2354*Significant at the 90% level235596235623572358===== PAGE 105 (score 128, numeric-paren 104, table-words 1, var-words 9) =====23592360Table 4.11: Model Averaged Estimates with Relative Coal Dependence Measure2361Variables2362Direct Effects2363Indirect Effects2364Total Effects2365Coal Production per Income 2366($)2367 0.0092***2368(0.00)2369 0.0465*2370(0.05)2371 0.0557**2372(0.03)2373Initial Income2374 0.6181***2375(0.00)2376-0.01832377(0.79)2378 0.5998***2379(0.00)2380Agriculture, Forestry, 2381Fisheries, Mining Emp.2382 -0.0154***2383(0.00)2384-0.01932385(0.14)2386 -0.0347***2387(0.00)2388Construction Emp.23890.00812390(0.39)2391-0.01412392(0.56)2393-0.00602394(0.85)2395Manufacturing Emp.2396 -0.0076**2397(0.03)23980.00632399(0.44)2400 -0.0016*2401(0.05)2402Transportation, 2403Communication and other 2404Public Utilities Emp.24050.00552406(0.32)2407-0.06912408(0.15)2409-0.06362410(0.16)2411Wholesale and Retail Trade 2412Emp.24130.00272414(0.79)2415 0.0462**2416(0.03)2417 0.0489**2418(0.04)2419Finance, Insurance and Real 2420Estate Emp.2421 0.0259***2422(0.00)24230.03282424(0.30)2425 0.0587**2426(0.03)2427Business and Repair 2428Services Emp.2429 0.0159***2430(0.00)24310.00912432(0.59)24330.02502434(0.70)2435Personal, Entertainment and 2436Recreation Services Emp.24370.00582438(0.16)24390.02212440(0.50)24410.02792442(0.41)2443Health Services Emp.24440.01002445(0.14)24460.02662447(0.42)24480.03662449(0.25)2450Educational Services Emp.2451 -0.0420***2452(0.00)2453 0.1185**2454(0.02)2455 0.0765**2456(0.04)2457Other Professional and 2458Related Services Emp.24590.00482460(0.38)2461-0.00312462(0.82)24630.00172464(0.88)2465Federal Government 2466Employment24670.00282468(0.30)2469 0.0477**2470(0.01)24710.05052472(0.11)2473State Government 2474Employment2475 -0.0193***2476(0.00)24770.00612478(0.49)2479-0.01322480(0.46)2481Local Government 2482Employment2483-0.01552484(0.11)2485-0.03642486(0.38)2487-0.05192488(0.21)2489Self Employment2490 0.0962***2491(0.00)2492-0.00502493(0.52)2494 0.0912***2495(0.00)2496Poverty2497 -0.0328***2498(0.00)24990.01852500(0.21)2501-0.01432502(0.26)2503Human Capital2504 0.1195***2505(0.00)2506 -0.0770**2507(0.01)2508 0.0425*** 2509(0.00)2510College town Dummy2511 -0.0171***2512(0.00)2513 -0.0759***2514(0.00)2515 -0.0930***2516(0.00)2517Population Density2518 -0.1904***2519(0.00)2520 -0.1070*2521(0.07)2522 -0.2974***2523(0.00)2524Area2525 -0.1995***2526(0.00)2527 -0.0944**2528(0.03)2529 -0.2939***2530(0.00)2531NAIX25320.00032533(0.61)2534 -0.0031*2535(0.08)2536-0.00282537(0.13)2538p-levels are reported in parenthesis. 2539***Significant at the 99% level2540**Significant at the 95% level2541*Significant at the 90% level254297254325442545===== PAGE 106 (score 9, numeric-paren 0, table-words 0, var-words 9) =====25462547State government employment in own-county has a significant negative impact on 2548own-county growth, suggesting an overexpansion of the state government. Unlike non-2549spatial models that find a negative association between federal government employment 2550and growth, the results of the spatial growth regressions suggest that federal government 2551employment in own-county do not hinder own-county growth. Indeed, although 2552insignificant, the sign of the federal government employment estimate switches from 2553negative to positive with the introduction of spatial dependence. 2554Self employment and poverty have positive and negative direct effects, 2555respectively, and no significant spillover effects. Human capital imposes a positive direct 2556impact and negative spillover effects, and its total impact is positive and significant at 99 2557percent level. Natural amenities index does not affect growth. Population density, which 2558captures the agglomeration effects, has a strong negative impact on own-county growth. 2559This result reflects the congestion effects and disamenities connected to large cities and 2560denser areas. Another result of interest is that college towns are associated with lower 2561income levels, capturing the impact of disproportionate number of students residing in 2562college towns. 25634.4 Conclusion2564The negative association between resource abundance and poor economic performance 2565has captured the attention of a wide range of audience, including academics, policy 2566makers and international organizations. While the presence and the possible causal 2567mechanisms of a resource curse have been extensively tested across countries, only a 2568limited literature has extended the analysis using U.S. data. This study fills the gap in the 2569982570