2016 3 15 JOURNAL OF SOUTH CHINA AGRICULTURAL UNIVERSITY SOCIAL SCIENCE EDITION a b a a. b. 400715 2009 ~ 2014 31 SDM 96. 26% F323. 6 A 1672-0202 2016 03-0063 - 13 1-2 20 70 1978 1984 2. 9% 7. 7% 134 355 47% 3 4 1984 1. 71 1 2014 2. 92 1 5 3% 6 2016-01 - 25 DOI 10. 7671 /j. issn. 1672-0202. 2016. 03. 008 15ZDA023 1986 E-mail 582916920@ qq. com
64 3 7 8-9 Rauch JE SMSAs 10 Glaeser Scheinkman Shleiferll 11 Henderson 12 Moretti 13 Zhang D Wu F 14 Abel 2-4 15 2004 16 20 80 90 17 1985-2005 18 1990 2000 2007 19 1995-2010 20 21 Liu 14 22 23 24 25
3 65 Y = F K AL Y K A L SAR SEM SDM SAR Y = λwy + Xβ + μ 2 SEM Y = λwy + Xβ + μ μ = ρwμ + ε 3 SDM Y = λwy + λwx + Xβ + μ 4 Y X W ρ λ β ε μ 1 2 3 4 SLM INCOME it = β 0 + β 1 HUM_CAP it + β 2 GDP it + β 3 LOAN it + β 4 FISCAL it + β 5 INVEST it + β 6 POWER it + β 7 LAND it + β 8 * W* INCOME it + μ it 5 SEM INCOME it = β 0 + β 1 HUM_CAP it + β 2 GDP it + β 3 LOAN it + β 4 FISCAL it + β 5 INVEST it + β 6 POWER it + β 7 LAND it + μ it μ it = ρ* W* μ it + ε it 6 5 6 2 3 4 i t INCOME 31 2009-2014 HUM_CAP 1
66 3 James Rauch 31 2009-2014 W* HUM_CAP 31 26 27 LOAN 28 29 Boehlje 30 FISCAL Barro 31 32 2009-2014 INVEST 33 34 2009-2014 POWER LAND 35 36 37 2009-2014 2009-2014 31 6* 31 186
3 67 1 INCOME 186 8. 51 0. 43 7. 59 9. 68 HUM_CAP 186 7. 27 0. 82 4. 05 9. 14 LOAN 186 8. 60 0. 74 6. 41 10. 27 FISCAL 186 6. 59 0. 88 3. 69 9. 27 INVEST 186 6. 35 1. 27 0. 00 7. 80 HVALUE 186 9. 33 0. 76 7. 98 11. 85 POWER 186 6. 86 0. 47 5. 86 7. 81 LAND 186 2. 43 2. 55 0. 26 12. 85 Arcgis Stata Moran Moran s I 38 Moran's I = n n i = 1 n i = 1 i j n W ij X i - X 珔 X j - X 珔 j = 1 7 n W j = 1 ij n X i - X 珔 2 i = 1 X i X j i j n 珔 X W ij 1 2 Moran'sI - 1 + 1 1 2 i j W ij 1 0 i j W ij 1 0
68 3-1 0 0 0 1 6 31 6 1 1 Moran 1 INCOME W_INCOME 31 * 31 0. 6001 H - H L - L 1 HUM_CAP W_HUM_CAP 31* 31 0. 3838 H - H L - L LISA Local indications of spatial association 39 Local Moran's I = 2 n X i - 珔 X n W ij X j - 珔 X i = 1 8 n X j - X 珔 2 i = 1
3 69 H - L L - H H - H L - L H - L L - H H - H L - L 2 Moran Moran p - Moran p - Moran p - Moran p - 1. 708 *** 0. 010 1. 557 ** 0. 011 0. 021 0. 880 0. 027 0. 859 1. 683 ** 0. 011 1. 188 * 0. 051 0. 043 0. 833-0. 025 0. 979 0. 038 0. 828 0. 563 0. 055-0. 068 0. 932 0. 164 0. 604 0. 153 0. 686 0. 449 0. 262 0. 306 0. 400-0. 043 0. 979 0. 048 0. 786 0. 038 0. 804-0. 016 0. 979 0. 151 0. 768 0. 007 0. 941 0. 248 0. 577 0. 239 0. 499 0. 050 0. 826 0. 013 0. 931 0. 177 0. 676 0. 449 0. 141 0. 727 ** 0. 015-0. 001 0. 961 0. 140 0. 782 0. 822 ** 0. 034 0. 359 0. 301 3. 693 *** 0. 000 0. 082 0. 853 1. 042 ** 0. 019 1. 672 *** 0. 000 1. 352 *** 0. 003 0. 033 0. 878 0. 879 ** 0. 047 2. 898 *** 0. 000 1. 446 *** 0. 000-0. 037 0. 992 0. 517 * 0. 067-0. 032 0. 996-0. 199 0. 647-0. 142 0. 750 1. 045 *** 0. 003 0. 322 0. 298 0. 628 0. 220-0. 011 0. 964 1. 051 ** 0. 018 2. 134 *** 0. 000-0. 021 0. 973 0. 025 0. 865 0. 614 0. 230 0. 084 0. 815 0. 095 0. 779 0. 080 0. 792 0. 890 * 0. 087-0. 980 * 0. 060 0. 042 0. 836 0. 195 0. 504 Moran Stata13. 0 P * 10% ** 5% *** 1% 40 - Elhorst 41 Lagrange multiplier LM 3 LM 5% LM LM LM LM 1 SAR SDM 1 Anselin - Florax 39 LM LM
70 3 OLS Pooled OLS Spatial fixed effects Time - period fixed effects 3 LM LM P LM SAR 17. 1709 0. 000 LM SEM 3. 3757 0. 066 LM robustsar 14. 2911 0. 000 LM robustsem 0. 4959 0. 481 LM SAR 105. 1160 0. 000 LM SEM 8. 8986 0. 003 LM robustsar 112. 7487 0. 000 LM robustsem 16. 5313 0. 000 LM SAR 1. 2282 0. 268 LM SEM 5. 1861 0. 023 LM robustsar 10. 2864 0. 001 LM robustsem 14. 2443 0. 000 LM SAR 16. 2797 0. 000 Spatial and Time - period fixed effects LM SEM 12. 6083 0. 000 LM robustsar 5. 4573 0. 019 LM robustsem 1. 7858 0. 181 LR Likelihood ratio 4 1% LR 4 LR LR P H0 H1 624. 1095 0. 0000 H0 H1 311. 0091 0. 0000 Hausman 5 1% 5 Hausman Hausman P H0 H1 24. 2752 0. 0606 LM LR Hausman INCOME it = β 0 + β 1 HUM_CAP it + β 2 GDP it + β 3 LOAN it + β 4 FISCAL it + β 5 INVEST it + β 6 POWER it + β 7 LAND it + β 8 * W* HUM_CAP it + β 9 * W* GDP it + β 10 * W* LOAN it + β 11 * W* FISCAL it + β 12 * W* INVEST it + β 13 * W* POWER it + β 14 * W* LAND it + s i + v t + μ it 9 v t s i μ it W 9 2009-2014
3 71 6 INCOME FE RE SAR SDM HUM_CAP 0. 163 *** 5. 01 0. 155 *** 6. 18 0. 007 0. 30-0. 010-0. 44 LOAN 0. 024 * 2. 02 0. 028 * 2. 46 0. 005 1. 69-0. 000-0. 13 FISCAL 0. 164 *** 10. 21 0. 148 *** 10. 98 0. 029 * 2. 53 0. 004 0. 47 INVEST - 0. 005-0. 22-0. 014-0. 89-0. 022-1. 74-0. 020-1. 58 HVALUE 0. 276 *** 12. 86 0. 305 *** 15. 36 0. 053 *** 3. 64 0. 092 * 2. 52 POWER - 0. 074-1. 82-0. 037-1. 07-0. 008-0. 33 0. 003 0. 14 LAND 0. 024 1. 42 0. 004 0. 45 0. 005 0. 51 0. 000 0. 03 _CONS 3. 943 *** 13. 60 3. 646 *** 15. 01 Spatial_rho 0. 858 *** 18. 57 0. 717 *** 13. 38 Variance Sigma2_e 0. 000 *** 4. 58 0. 000 *** 4. 51 W* HUM_CAP 0. 065 ** 3. 08 W* LOAN 0. 002 0. 23 W* FISCAL 0. 030 * 2. 07 W* INVEST 0. 082 ** 2. 76 W* HVALUE - 0. 032-1. 09 W* POWER - 0. 050-1. 39 W* LAND 0. 019 1. 24 N 186 186 186 186 珔 R 2 0. 94 0. 562 0. 654 t * p < 0. 05 ** p < 0. 01 *** p < 0. 001 6 0. 163 0. 024 0. 164 0. 276 0. 024 5% 0. 1% 0. 94 6 SAR SDM Spatial_rho W* INCOME
72 3 5% W* HUM_CAP W* INCOME SAR Spatial_rho W* INCOME 0. 8580. 1% SDM Spatial_ rho W* INCOME 0. 717 0. 1% SDM W* HUM_CAP 0. 065 1% W* LOAN W* FISCAL W* INVEST W * LAND W* FISCAL 5% W* INVEST 1% W* HVALUE W* POWER 7 SAR SDM SAR SDM SAR SDM HUM_CAP 0. 008 0. 29 0. 006 0. 27 0. 015 0. 13 0. 180 * 2. 39 0. 023 0. 16 0. 187 * 2. 03 LOAN 0. 007 1. 67-0. 000-0. 04 0. 028 1. 53 0. 000 0. 01 0. 036 1. 58 0. 000 0. 01 FISCAL 0. 042 * 2. 55 0. 014 1. 53 0. 161 * 2. 27 0. 107 ** 2. 97 0. 204 * 2. 40 0. 121 ** 2. 99 INVEST - 0. 032-1. 78 0. 001 0. 06-0. 129-1. 55 0. 238 * 2. 36-0. 161-1. 61 0. 239 * 2. 12 HVALUE 0. 078 *** 5. 32 0. 105 ** 3. 27 0. 302 *** 4. 63 0. 105 ** 3. 17 0. 381 *** 5. 50 0. 210 *** 7. 42 POWER - 0. 007-0. 21-0. 009-0. 32-0. 025-0. 16-0. 168-1. 41-0. 032-0. 17-0. 176-1. 28 LAND 0. 006 0. 37 0. 005 0. 45 0. 028 0. 37 0. 066 1. 43 0. 035 0. 37 0. 071 1. 46 t * p < 0. 05 ** p < 0. 01 *** p < 0. 001 7 SAR SDM SAR SDM 0. 006 0. 014 0. 105 0. 005
3 73 0. 163 0. 164 0. 276 0. 024 26. 17% 10. 71% 1. 63% 3. 8% 1% 2009-2014 2 SDM 1 0. 18 0. 17 0. 238 0. 105 0. 066 96. 26% 88. 43% 99. 58% 50% 92. 96% 2 5% 1% 3 2009-2014 31 96. 26% 2009-2014 1 2 3 26. 17% 96. 26% 4 99. 58% 88. 43% 1 2 3 6 Spatial_rho 0. 717
74 3 1 SOLOW R M. A Contribution to the Theory of Economic Growth J. Quarterly Journal of Economics 1956 70 1 65-94. 2. M.. 2014. 3 LIN J Y. Rural Reforms and Agricultural Growth in China. J. American Economic Review 1992 82 1 34-51. 4. M. 2012. 5. J. 2015 7 82-97. 6. J. 2010 4 4-13. 7. J. 2014 2 79-90. 8 WHEELER C H. Search Sorting and Urban Agglomeration J. Journal of Labor Economics 2001 19 4 879-899. 9 GLAESER E L RESSEGER M G. The Complementarity Between Cities and Skills J. Journal of Regional Science 2010 50 1 221-244. 10 RAUCH J E. Productivity Gains from Geographic Concentration of Human Capital Evidence from the Cities J. Journal of Urban Economics 1993 34 3 380-400. 11 GLAESER E L SCHEINKMAN J A SHLEIFER A. Economic Growth in a Cross - Section of Cities J. Journal of Monetary Economics 1995 36 1 117-143. 12 HENDERSON J V WANG H G. Urbanization and City Growth J. Regional Science & Urban Economics 2004 37 3 283-313. 13 MORETTI E. Human Capital Externalities in Cities J. Handbook of regional and urban economics 2004 4 2243-2291. 14 ZHANG J WU F ZHANG D et al. A Study on Labor Mobility and Human Capital Spillover J. China Agricultural Economic Review 2009 1 3 342-356. 15 ABEL J R ISHITA D GABE T M. Productivity and the Density of Human Capital J. Journal of Regional Science 2012 52 4 562-586. 16. J. 2006 11 72-81. 17. J. 2004 1 33-44. 18. J. 2008 5 47-57. 19. J. 2011 2 691-708. 20. J. 2013 4 54-62. 21. J. 2015 3 185-197. 22 LIU Z. Human Capital Externalities in Cities Evidence from Chinese Manufacturing Firms J. Journal of Economic Geography 2014 14 3 621-649. 23. J. 2010 3 28-33. 24. J. 2012 5 21 -
3 75 25. 25. 2001-2011 J. 2013 11 12-19. 26 HAYS D GOLDSMITH T H. Microspectrophotometry of the Visual Pigment of the Spider Crab Libinia Emarginata J. Zeitschrift für vergleichende Physiologie 1969 65 2 218-232. 27. J. 2005 9 30-43. 28. 1952-2002 J. 2005 10 18-27. 29. J. 2011 10 110-122. 30 BOEHLJE M D LINS D A. Risks and risk Management in an Industrialized Agriculture J. Agricultural Finance Review 1998 58 1-16. 31 BARRO R J. Inequality and Growth in a Panel of Countries J. General Information 2000 5 1 5-32. 32. J. 2005 12 4-14. 33. J. 2002 15 9-16. 34. J. 2013 3 27-32. 35. M.. 2003. 36. J. 2003 24 6 50-55. 37. J. 2005 8 4-9. 38 MORAN P A. A Test for the Serial Independence of Residuals J. Biometrika 1950 37 1-2 178-181. 39 ANSELIN L. Local Indicators of Spatial Association - LISA J. Geographical analysis 1995 27 2 93-115. 40 ELHORST J P. Spatial Econometrics From Cross - sectional Data to Spatial Panels M. New York Springer 2014. 41 ELHORST J P. Applied Spatial Econometrics Raising the Bar J. Spatial Economic Analysis 2010 5 1 9-28. A Spatial Econometric Analysis on the Relationship between Human Capital and Rural Residents' Incomes LIU Wei a ZHANG Ying-liang b TIAN Hong-yu a a. School of Economics and Management b. Research Center of Rural Economics and Management Southwest University Chongqing 400715 China Abstract Based on 2009-2014 statistical data of 31 provinces using statistical methods of Spatial Dubin Model with time-period specific effects SDM we analyzed the spillover effect of the relationship between human capital investment and rural residents' income. The results show that the human capital and rural residents' incomes have a strong spillover effects the impact of human capital investment on rural residents' income mainly from the spillover effect of human capital investment spillover effects account for 96. 26% of the total effect. Key Words human capital investment rural residents' income spatial correlation spatial heterogeneity