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地理科学    2016, Vol. 36 Issue (2): 161-169     DOI: 10.13249/j.cnki.sgs.2016.02.001
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
中国大城市拆迁安置居民补偿方式与受益率测度 ——以南京为例
孙东琪1(),张京祥2(),陈浩2,胡毅3
1.中国科学院地理科学与资源研究所,北京 100101
2.南京大学建筑与城市规划学院,江苏 南京210093
3.住房和城乡建设部城乡规划管理中心,北京100835
The Measure of Displaced Households’ Compensation Approaches and Benefits Rate Based on Regression Model: A Case of Nanjing
Dongqi Sun1(),Jingxiang Zhang2(),Hao Chen2,Yi Hu3
1.Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Science,Beijing,100101,China
2.School of Architecture and Urban Planning, Nanjing University, Nanjing 210093, Jiangsu, China
3.Urban-rural Planning Management Center, Ministry of Housing and Urban-Rural Development, Beijing 100835, China)
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摘要 

拆迁安置补偿是城市内城改造与扩张过程中涉及拆迁居民利益的核心问题,拆迁安置家庭多个特征因影响补偿方式和受益率。针对补偿方式不同与受益率不同,分别采用二项逻辑回归与多元线性回归模型,对南京市4个安置区的349户拆迁安置居民家庭进行了实证分析,结果表明:① 对于实物补偿和货币补偿,城中村拆迁安置居民家庭和夫妻双方职业均为“无业”的家庭更容易获得实物补偿。② 单从经济角度来讲,城中村拆迁安置家庭成为拆迁安置家庭中的最大受益者,7人及以上的大家庭、高学历家庭也是货币补偿中的受益者。但其受益并非是由市场化的结果造成,而是市场作用与计划经济旧有制度的共同使然。因此,建立公平合理的补偿机制是解决问题的关键。

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孙东琪
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陈浩
胡毅
关键词 回归模型拆迁安置补偿方式受益率南京 
Abstract

Displaced compensation is the main point of displaced households’ benefits in inner-city restructuring and urban expansion. Its compensation approaches and benefits rate are influenced by many factors. This article uses 349 displaced residential surveys conducted in 4 resettlement areas in Nanjing to examine the compensation. 1) Examining compensation approaches by using binary logistic regression. In China, there are two approaches: in-kind compensation and monetary compensation. The result shows that displaced households from urban village and households with the couple are both unemployed are more likely to get in-kind compensation. Because of the large size of their displaced housing area in urban village, the monetary expenses are too high to the local government. And the resettlement housing is founded by the central government and municipal government subsidies, not by the local government alone, therefore, the local government are more likely to offer in-kind compensation for displaced households from urban village. 2) Examining the benefits rate of displaced households who get monetary compensation. The results show that: households from urban village have the highest benefits rate, because before displacement they could get more income from renting their housing, the local government have to give more to persuade them to leave. And “more than 7 members’ large household size” and “high education level” have significant positive contribution to benefits rate. For the large size households, the local government could consider their actual situation to give more benefits. And for the high education households, they know more law and policies to negotiate with the local government to get more. But the household with stated-work unit family member get less benefit rate. The results of this analysis could have several explanations. One is that residents who held a position in a work unit are usually better off than farmers. They are able to accept a lower discount than farmers, and may even purchase market housing. Another explanation is that since work units are supported by corresponding ministries of the central government, or have to maintain good relationships with the local government, in some demolition projects the local government coordinates with work units to make sure that employees are forced to move or to accept and sign the compensation contracts as soon as possible which make them loss the negotiation chance. We conclude that the benefit is not the result of market-oriented, but the product of the market and the old system of planned economy. Because in the process of compensation, the local government still use some the planned economy period approaches, such as incomplete monetization, set the standard ceiling; consider the households actual situation (whether they are low-income), etc.. Market led by the demolition and land transfer, in the resettlement compensation is still the implementation of non-market approach. Therefore, the establishment of fair and reasonable compensation mechanism is the key solution.

Key wordsregression model    displacement and resettlement    compensation approaches    benefits rate    Nanjing
收稿日期: 2015-03-01      出版日期: 2016-06-06
基金资助:国家自然科学基金资助项目(51578276,41501137)资助
作者简介: 孙东琪(1985-),男,山东单县人,博士后,主要研究方向经济地理,城市与区域规划.E-mail:sundq@igsnrr.ac.cn
引用本文:   
孙东琪, 张京祥, 陈浩等 . 中国大城市拆迁安置居民补偿方式与受益率测度 ——以南京为例[J]. 地理科学, 2016, 36(2): 161-169.
Dongqi Sun, Jingxiang Zhang, Hao Chen et al . The Measure of Displaced Households’ Compensation Approaches and Benefits Rate Based on Regression Model: A Case of Nanjing[J]. SCIENTIA GEOGRAPHICA SINICA, 2016, 36(2): 161-169.
链接本文:  
http://geoscien.neigae.ac.cn/CN/10.13249/j.cnki.sgs.2016.02.001      或      http://geoscien.neigae.ac.cn/CN/Y2016/V36/I2/161
名称 住房建设
面积(hm2)
总住房量
(套)
首批入住
时间
2011年市
场价格
距市
中心
尧林仙居 34.5 6700 2003年 8900元/m2 14 km
百水芊城 166.8 13253 2004年 9400元/m2 12 km
银龙花园 54.9 5898 2002年 11800元/m2 8 km
西善花苑 37.32 --- 2007年 6500元/m2 18 km
Table 1  研究区域基本情况
Fig.1  本文调研的安置区的分布
受益率(%) 0~10 10~20 20~40 40~50 50~60 60~80 80~99.9 实物补偿 总计
个数(个) 0 8 41 90 76 45 22 67 349
百分比(%) 0 2.3 11.7 25.8 21.8 12.9 6.3 19.2 1
Table 2  货币补偿的安置居民家庭受益率分布
年份 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
南京平均价格 2346 2795 2907 2939 3148 3516 4072 4477 5304 6153 7185 10154 11200
尧林仙居 1869 2225 2314 2332 2501 2795 3240 3560 4219 4895 5696 8099 8900
百水芊城 1974 2350 2444 2463 2641 2952 3422 3760 4456 5170 6016 8554 9400
银龙花园 2478 2950 3068 3092 3316 3705 4295 4720 5593 6490 7552 10738 11800
西善花苑 1365 1625 1690 1703 1827 2041 2366 2600 3081 3575 4160 5915 6500
Table 3  保障性安置区住房标准化后的市场价格(元/m2
变量 变量类型 百分比或均值 N
受益率 实物补偿 19.2% 67
<100% (货币补偿) 80.8 % 282
拆迁前土地类型及区位类型 虚拟变量 集体土地&郊区(一般农村)——参照变量 64.2% 224
集体土地&主城(城中村) 26.9% 94
国有土地&郊区 1.7% 6
国有土地& 主城 7.2% 25
拆迁前家庭人口数 虚拟变量 小家庭(1~3人) ——参照变量 47.6% 166
中等规模家庭 (4~6人) 45.0% 157
大家庭(7人及以上) 7.4% 26
拆迁前住房面积 连续变量 188 m2 ---
家庭学历 虚拟变量 低学历(高中以下) ——参照变量 49.6% 173
高学历(高中及以上) 50.4% 176
家庭职业类型 虚拟变量 农民——参照变量 55.7% 194
国有企业或事业单位
保持就业 8.3% 29
退休、下岗 12% 42
私人或外资公司
保持就业 8% 28
退休、下岗 5.4% 19
一直无业 10.6% 37
Table 4  自变量与因变量的基本统计情况
系数 (B) 标准误差 Exp(B)
(常数项) -1.336*** 0.149 0.203
拆迁前土地类型及区位类型(参照变量:集体土地&郊区)
集体土地&主城 1.302*** 0.310 3.000
国有土地&主城/郊区 -0.136 0.428 0.681
拆迁前住房面积 -0.003 0.006 0.893
拆迁前家庭人口数(参照变量:小家庭1~3人)
中等规模家庭 (4~6人) -0.267 0.352 0.579
大家庭(7人及以上) -0.106 0.644 0.730
家庭学历(参照变量:低学历(高中以下))
高学历(高中及以上) 0.472 0.489 1.562
家庭职业类型(参照变量: 农民)
国有企业或事业单位
保持就业 -0.242 0.568 0.531
退休、下岗 0.573 0.398 1.781
私人或外资公司
保持就业 0.301 0.526 1.330
退休、下岗 -0.641 0.803 0.662
一直无业 0.833* 0.362 2.479
R2 0.197
样本数N 349
Table 5  二项逻辑回归结果
标准1 标准2 标准3 标准4
拆迁房屋面积标准 <28 m2/人 28~42 m2/人 42~56 m2/人 >56 m2/人
补偿房屋面积标准 19 m2/人 21 m2/人 25 m2/人 28 m2/人
Table 6  南京市集体土地拆迁房屋置换的面积标准
系数 (B) 标准误差
(常数项) 47.839*** 1.194
拆迁前土地类型及区位类型
(参照变量:集体土地&主城)
集体土地&主城 18.660*** 2.433
国有土地&主城/郊区 -3.058 2.348
拆迁前住房面积 -0.013 0.029
拆迁前家庭人口数(参照变量:
小家庭1~3人)
中等规模家庭 (4~6人) 3.550 1.628
大家庭(7人及以上) 6.739** 2.726
家庭学历[(参照变量:低学历
(高中以下)]
高学历(高中及以上) 4.789*** 2.761
家庭职业类型(参照变量: 农民)
国有企业或事业单位
保持就业 -4.880* 2.186
退休、下岗 -2.101 3.164
私人或外资公司
保持就业 1.014 2.579
退休、下岗 -4.762 2.763
一直无业 -2.133 3.017
R2 0.220
样本数N 282
Table 7  多元线性回归的结果
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