2018 4 * 400716 DOI:10.13246/j.cnki.jae.2018.04.005 20142017 2009 2. 16 2014 5. 4 3. 43% 6. 22% 2. 79 2009 1. 46 2014 3. 4 5. 08% 3. 81% 1. 27 GDP 9. 17% ** * ** 11&ZD047 14AZ034 13CFX080 CYB16057 2010 2014 2014 2015 54
Feder 1991 2006 2009 Chapple 2002 Barslund 2008 2006 2010 2010 2011 Stiglitz 1981 2009 2011 Klychova 2014 2016 2002 2010 2003 2011 2014 Pal 2015 2014 55
2018 4 * ** *** * ** 2010 2015 4. 47 33. 30% *** 56
2006 2016 2010 2017 1 2 2015 17 70 5 10 50 2013 3500 68 3318 94. 80% 3162 1. * 2. 1 * 57
2018 4 1 = 1 = 0 = 1 = 0 = 1 = 0 = 1 = 0 = 1 = 0 = 1 = 2 = 3 = 4 = 5 = 3 = 2 = 1 3. 1. 2 2. 3 268 88 324 252 32. 83% 77. 78% 81 30 42 36 37. 04% 85. 71% 271 136 150 128 50. 18% 85. 33% 58
2 0. 1631 0. 3694 0 1 3162 0. 1963 0. 3976 0 1 3162 0. 1316 0. 3383 0 1 3162 0. 0802 0. 2726 0 1 3162 0. 9266 0. 2068 0 1 3162 48. 1758 11. 0290 20 86 3162 2442. 493 1092. 135 400 7396 3162 0. 9628 0. 1890 0 1 3162 2. 7435 0. 9345 1 5 3162 10. 7678 0. 9482 6. 9077 14. 2855 3162 0. 7580 0. 2996 0 1 3162 10. 1563 1. 0047 7. 3132 14. 2855 3162 6. 5371 8. 9326 0 236 3162 0. 2201 0. 3353 0 1 3162 0. 1068 0. 2926 0 1 3162 7. 7900 1. 0566 3. 9120 10. 8197 3162 0. 6304 0. 4829 0 1 3162 11. 417 1. 2761 0 1 3162 0. 2968 0. 4570 0 1 3162 3 268 88 324 252 81 30 42 36 271 136 150 128 620 254 516 416 1. loan s = γ 0 loan d = α 0 i = 1 i = 1 γ i β i α i β i j = 1 j = 1 γ j β j α j β j l = 1 l = 1 γ l β l + id s + ε s 1 α l β l + id d + ε d 2 loan s loan d β i β j β l id s id d γ 0 α 0 ε s ε d 2. Probit 59
2018 4 Probit * 4 5 chi 1 H 0 ρ = 0 Probit 4 0. 0447 0. 1427 0. 3084 0. 1691 0. 5023 ** 0. 2045 0. 2287 0. 2499 0. 2402 ** 0. 1069 0. 1208 * 0. 1355-0. 2355 0. 1634-0. 3557 * 0. 2146 0. 0043 0. 0249 0. 0131 0. 0311-0. 0001 0. 0002 0. 0001 0. 0003 0. 1531 0. 2722 0. 3154 0. 2289 0. 1296 *** 0. 0517 0. 1246 * 0. 0668-0. 1076 * 0. 0653 0. 0463 * 0. 0841-0. 2459-1. 21-0. 179 0. 2793 0. 1120 * 0. 0591 0. 2738 *** 0. 0646 0. 0006 0. 0026 0. 0043 0. 3751 0. 1085 ** 0. 0805 0. 1223 ** 0. 0502-1. 0845 0. 8231-5. 2228 *** 1. 0842-700. 9186 chi 1 115. 605 0. 0000 *** ** * 1% 5% 10% 2002 * Probit 60
2014 1 5 0. 0480 0. 1472 0. 3281 0. 1392-0. 0064 0. 2266 0. 3838 * 0. 2108 0. 2269 ** 0. 1099 0. 2516 ** 0. 1142 0. 1199 0. 1987-0. 3401 0. 1957 0. 0441 0. 0283 0. 0617 0. 0355-0. 0005 * 0. 0002-0. 0007 0. 0003-0. 0567 0. 1776 0. 1287 0. 1771 0. 0174 0. 0556 0. 0403 0. 0536-0. 3157 *** 0. 0687 0. 2584 *** 0. 0710-0. 1158 0. 2147-0. 0311 0. 2060 0. 2524 *** 0. 0619 0. 4237 *** 0. 0614 0. 0038 0. 0067-0. 0001 0. 0066-0. 0348 0. 0933-0. 0391 0. 0411-1. 1505 0. 8496-3. 6113 *** 0. 9214-833. 2892 chi 1 183. 335 0. 0000 2009 2014 2006 2011 2016 2010 61
2018 4 2. 2 U 2009 2011 2010 2010 * * 62
1 2 3 1 2 3 1. Barslund Mikkel and Formal Tarp. Formal and Informal Rural Credit in Four Provinces of Vietnam. The Journal of Development Studies 2008 4 485~ 503 2. Feder Gershon and David Feeny. Land Tenure and Property Rights Theory and Implications for Development Policy. The World Bank E- conomic Review 1991 1 135~ 153 3. Klychova G S et al. Priorities of Agricultural Credit Cooperation Development. Mediterranean Journal of Social Sciences 2014 18 215 4. Pal Debdatta and Arnab K. Laha. Sectoral Credit Choice in Rural India. Journal of Choice Modelling 2015 14 1 ~ 16 5. Stiglitz Joseph E and Andrew Weiss. Credit Rationing in Markets with Imperfect Information. The American Economic Review 1981 71 393 ~ 410 6... 2010 12 190 ~ 206 7.. 1874. 2009 5 73~ 82 8... 2010 240~ 244 9... 2002 12 68~ 72 10... 2014 3 48~ 57 11... 2011 10 100~ 111 12... 2010 10 16 ~ 26 56. 13... 2010 1 74 ~ 87 14... 2014 11 145~ 158 15... 2011 7 34~ 41 16... 2012 4 155 ~ 168 63
2018 4 17... 2017 5 36~ 45 110~ 111. 18... 2006 5 167~ 180 19... 2016 2 111~ 125 20.. 11 1664. 2014 3 45 ~ 56 21... 2014 4 93~ 105 22... 2003 28~ 30 23... 2011 7 98~ 113 24... 2006 3 37 ~ 49 Reinterpret the Supply and Demand relation of China Agricultural Credit Market in the New Normal Based on perspective of farmers differentiation ZHANG Ziyu WEN Tao WANG Xiaohua Abstract Based on the perspective of rural households differentiation this article uses micro - survey data to reinvestigate the relations between supply and demand in China s agricultural loan market under the new normal. The results show that 1 In the new normal farmers demand for agricultural loan is largely income-oriented.the informal loan supplies perform better than formal sources in terms of meeting farmers needs 2 the newly e- merged agricultural production forms devoted into entrepreneurship are more likely succeed in loan application than other forms.the implication of this study are as follows agriculture credit market in China should not be exclusive integrating the comparative advantages from both formal and informal credit suppliers could be a future direction.moreover agriculture credit market should adapt to the dynamic changes of farmers credit demand especially exploring an effective approach to the financing mortgage of land contract management right. Keywords New normal Agricultural credit market Supply and demand relation 64