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浙江大学学报(理学版)  2022, Vol. 49 Issue (5): 633-641    DOI: 10.3785/j.issn.1008-9497.2022.05.015
城市科学     
隐性债务对城市空间扩张的影响研究
张娟锋,李乐乐,张丹霞,湛东升()
浙江工业大学 管理学院,浙江 杭州 310023
The impact of implicit debt on cities′ spatial expansion
Juanfeng ZHANG,Lele LI,Danxia ZHANG,Dongsheng ZHAN()
The College of Management,Zhejiang University of Technology,Hangzhou 310023,China
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摘要:

为推动城市开发与基础设施建设,满足城市新增人口的公共服务需求,地方隐性债务规模快速增长,经济下行加剧了地方政府所面临的债务风险。利用2007—2018年我国285个地级市隐性债务数据,采用固定效应模型,从债务规模与发债主体特征两个视角评估隐性债务对城市空间扩张的驱动机制。主要结论有:(1)隐性债务规模对城市空间扩张有显著正向影响,上期隐性债务规模的增加将推动当期城市建成区扩张。发债规模增加1亿元,城市建成区面积增加0.192 km2,“借债建城”成为地方政府推动城市空间扩张的主要模式。(2)发债企业数和发债企业质量对城市空间扩张有显著正向影响,发债企业数增加,且信用评级为AA+及以上,发债主体的比例提高推动城市建成区面积的增加。信用等级高的发债企业越多,城市扩张能力越强。(3)发债企业数对城市空间扩张的影响存在区域异质性,发债企业数对中西部地区的城市空间扩张具有显著正向影响,对东部地区则无显著影响。

关键词: 空间扩张隐性债务融资平台地方政府    
Abstract:

In order to promote urban development and infrastructure construction, and supply the public goods for the increasing urban population, the scale of the local implicit debt is growing rapidly. With the economics depression, the debt risk of local governments is high. Based on the perspective of the debt scale and issuers' characteristics, this paper uses the data of implicit debt in 285 prefecture-level cities from 2007 to 2018, and uses a fixed-effect model to evaluate the driving mechanism of implicit debt on cities' spatial expansion. There are three conclusions: (1) The scale of implicit debt has a significant positive impact on the expansion of urban space. The increase in the scale of implicit debt in the previous period will promote the expansion of urban built-up areas in the current period. With The scale of debt increasing 100 million RMB, the urban built-up area would increase by 0.192 km2. "Borrowing money to build the city" is becoming the main mode for local governments. (2) The number and rating of bond issuers have a positive and significant impact on urban space expansion. The increase in the number of bond issuers and in the proportion of bond issuers with a credit rating of AA+ and above will promote the increase in urban built-up areas. More local financing platforms with high credit are capable of promote urban expansion. (3) The influence of the number of bond-issuing companies has regional heterogeneity. It has a positive and significant impact on urban spatial expansion in the central and western regions, while has no significant impact in the eastern region.

Key words: spatial expansion    implicit debt    financing platform    local government
收稿日期: 2021-07-09 出版日期: 2022-09-14
CLC:  F 323.24  
基金资助: 浙江省社科规划项目(22NDJC053YB);杭州市哲学社会科学规划项目(Z20JC099);浙江工业大学人文社科类基本科研业务费项目(GB202003004);国家社科基金项目(16BJL053)
通讯作者: 湛东升     E-mail: zhands@126.com
作者简介: 张娟锋(1979—),ORCID:https://orcid.org/0000-0003-4917-1372,男,博士,教授,主要从事房地产经济和城市经济学研究.
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引用本文:

张娟锋, 李乐乐, 张丹霞, 湛东升. 隐性债务对城市空间扩张的影响研究[J]. 浙江大学学报(理学版), 2022, 49(5): 633-641.

Juanfeng ZHANG, Lele LI, Danxia ZHANG, Dongsheng ZHAN. The impact of implicit debt on cities′ spatial expansion. Journal of Zhejiang University (Science Edition), 2022, 49(5): 633-641.

链接本文:

https://www.zjujournals.com/sci/CN/10.3785/j.issn.1008-9497.2022.05.015        https://www.zjujournals.com/sci/CN/Y2022/V49/I5/633

图1  2007—2018年我国285个地级市城投债发行支数和发债规模注 数据采自Wind数据库,为避免债券重复,剔除了各地级市城投公司在上海、深圳上市的企业债,计算得到285个地级市(不含港、澳、台)2007—2018年发行的城投债支数与发债规模。
变量类别变量测量符号单位样本量均值标准差最小值最大值
城市空间外扩建成区面积Areakm23 420115.911136.6866.5701300.010
地方隐性债务发债规模LScale亿元3 13528.28180.2430.000904.300
发债企业数LQuantity3 1351.5203.4220.00041.000
发债企业质量LQuality%3 13515.89132.1150.000100.000
社会经济人均GDP(取对数)ln PCGDP3 42010.4230.6758.13112.456
外商直接投资(加1后取对数)ln Invest万美元3 4209.2442.9690.00014.152
城市产业结构Structure%3 42086.8518.22139.01099.970
人口人口数(取对数)ln People万人3 4204.2050.8491.6097.196
人口密度(取对数)ln Popden人·km-23 4208.0000.7425.5139.908
区域交通轨道交通建设Rail虚拟变量3 4200.0510.2200.0001.000
城市道路面积(取对数)ln Road万m23 4206.8630.9663.2969.839
表1  各变量的描述性统计
变量模型1模型2模型3模型4
LScale

0.222***

(29.959)

0.192***

(13.922)

LQuantity

4.855***

(25.845)

0.859**

(2.534)

LQuality

0.054***

(2.822)

0.044***

(2.666)

ln PCGDP

13.302***

(7.201)

12.102***

(6.295)

21.774***

(10.374)

12.189***

(6.525)

Structure

-0.799***

(-3.864)

-0.847***

(-3.967)

-1.370***

(-5.805)

-0.760***

(-3.677)

ln Invest

-0.038

(-0.137)

-0.079

(-0.279)

-0.084

(-0.267)

-0.046

(-0.168)

ln People

49.379***

(14.851)

47.650***

(13.813)

62.075***

(16.432)

48.539***

(14.551)

ln Popden

-3.426***

(-3.186)

-3.526***

(-3.177)

-4.433***

(-3.600)

-3.390***

(-3.159)

Rail

42.915***

(14.116)

50.157***

(16.233)

76.452***

(23.628)

42.627***

(14.045)

ln Road

11.896***

(8.554)

11.983***

(8.347)

13.458***

(8.451)

11.728***

(8.446)

_cons

-222.467***

(-13.174)

-199.305***

(-11.246)

-313.827***

(-16.471)

-210.877***

(-12.255)

城市效应YesYesYesYes
年份效应NoNoNoNo
N3 1353 1353 1353 135
R20.620 70.595 90.502 30.622 5
F581.32523.84358.53468.37
表2  285个地级市的面板数据回归结果
变量模型5(东部地区)模型6(中西部地区)
LScale

0.177***

(7.449)

0.193***

(11.259)

LQuantity

-0.542

(-0.975)

3.154***

(6.951)

LQuality

0.043

(1.308)

0.005

(0.319)

ln PCGDP

18.970***

(3.826)

9.245***

(5.594)

Structure

-2.415***

(-3.141)

-0.353**

(-2.082)

ln Invest

-0.254

(-0.242)

0.100

(0.449)

ln People

50.830***

(7.425)

38.891***

(11.860)

ln Popden

2.878

(0.899)

-3.686***

(-4.059)

Rail

45.585***

(8.976)

28.909***

(7.632)

ln Road

35.642***

(8.794)

5.695***

(4.799)

_cons

-344.554***

(-5.952)

-147.035***

(-10.089)

城市效应YesYes
时间效应NoNo
N1 0672 068
R20.610 70.697 3
F150.59430.86
表3  东部和中西部地区面板数据的回归结果
变量模型7模型8模型9模型10
LScale

0.233***

(4.714)

0.397**

(2.547)

LQuantity

3.937***

(3.702)

-5.436*

(-1.695)

LQuality

0.786***

(6.698)

0.660***

(5.028)

ln PCGDP

9.058***

(3.363)

9.155***

(3.222)

9.634***

(3.173)

7.890**

(2.532)

Structure

-0.380**

(-2.358)

-0.371**

(-2.265)

-0.604***

(-3.492)

-0.522***

(-3.145)

ln Invest

-3.909***

(-9.731)

-3.986***

(-9.956)

-4.060***

(-9.221)

-3.825***

(-9.226)

ln People

93.758***

(24.779)

94.603***

(25.203)

89.188***

(23.171)

87.342***

(23.906)

ln Popden

-3.541***

(-2.846)

-3.340***

(-2.687)

-5.230***

(-3.678)

-5.595***

(-4.069)

Rail

177.608***

(10.260)

188.707***

(11.237)

196.271***

(12.289)

168.896***

(9.814)

ln Road

14.332***

(5.259)

14.197***

(5.164)

12.106***

(4.641)

12.229***

(4.802)

_cons

-389.653***

(-15.524)

-395.263***

(-14.759)

-333.665***

(-11.381)

-314.312***

(-10.741)

N2 8502 8502 8502 850
R20.792 70.790 50.780 20.787 2
Adj R20.792 10.789 90.779 50.786 4
F279.38295.20264.02251.93
表4  稳健性检验结果
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