中国城市土地财政扩张及对经济效率影响路径

doi:10.11821/dlxb202010007

摘要/Abstract

摘要：

土地财政对经济效率的影响过程具有系统性、复杂性特征。本文考虑效率提升的结构性特征,从规模经济效应、技术抑制效应等方面构建土地财政对全要素生产率影响机制,进而从投资挤占效应、成本拉动效应探索其传导路径,并基于2005—2015年中国285座地级以上城市面板数据集,运用固定效应模型对其影响过程机理与区域分异特征进行实证检验。结果表明：① 2005—2015年间,中国城市土地财政规模持续扩张,并由沿海城市向内陆城市扩散,但城市全要素生产率增长率却出现波动下降趋势。② 整体上,土地财政对城市全要素生产率产生显著的负向影响,且对西部、中部、东北等地区以及中小城市、Ⅰ型大城市、Ⅱ型大城市的影响依次增强,而东部地区与超特大城市并不显著。③ 土地财政对经济效率影响的技术抑制效应显著大于规模经济效应,中部地区、Ⅰ型、Ⅱ型及超特大城市尤其严重。④ 具体传导路径为,土地财政扩张主要通过房地产开发带来的投资挤占效应影响城市全要素生产率,其次是房价上涨的成本拉动效应,而工资水平上升主要在东部地区、Ⅰ型及以下规模城市发挥作用。以上结论在一系列稳健性检验后依然成立。最后提出明确土地出让收支范围、产业梯度转移与升级、宏观调控与因城施策并行等政策建议。

关键词: 土地财政, 全要素生产率, 规模经济效应, 技术抑制效应, 投资挤占效应, 成本拉动效应

Abstract:

Land finance has been favored by local governments along with the rapid expansion of urbanization and industrialization, but it draws the attention of the central government recently and then tends to be alleviated. The influencing process of land finance on economic efficiency is systematic and complex. Considering the structural characteristics of economic efficiency growth, this paper constructs the multi-level mechanism and path of land finance's influence on economic efficiency, from the aspects of scaling effect, technical-inhibition effect, investment crowding-out effect and cost-pulling effect. Then this paper employs the fixed effect model to empirically test the impact and regional differences of land finance on total factor productivity (TFP) of 285 Chinese cities from 2005 to 2015. The results show that: Firstly, China's urban land financial scale continued to expand, and spread from the coastal cities to the inland cities in the study period. At the same time, the urban TFP growth rate presents a fluctuation downward tendency. Secondly, the baseline regression shows that the land finance expansion has a significant negative impact on the growth of urban TFP. And the impact on western, central, northeastern China and small and medium-sized cities, I type and II type cities gradually increase in order, while the impact is not significant in eastern China and large cities with a population of three million or more. Thirdly, although the land finance has a certain scale economic effect on regional development, it mainly inhibits technological progress to directly hinder the TFP growth. And it is more serious in central and western China and cities with a population more than one million. Further, it is mainly through investment crowding-out effect brought by the real estate development that the land finance affects urban TFP, followed by the cost-pulling effect by house prices rising, while wages rising mainly affects urban TFP in eastern China and cities with a population less than three million. The above conclusions are still valid after a series of robustness tests. Finally, this paper puts forward some policy suggestions, such as defining the scope of revenue and expenditure of land finance, gradient transfer and upgrading of industries and macro-control and implementing targeted policies to each city.

Key words: land finance, total factor productivity, scaling effect, technical-inhibition effect, investment crowding-out effect, cost-pulling effect

李汝资, 刘耀彬, 王文刚, 谢德金. 中国城市土地财政扩张及对经济效率影响路径[J]. 地理学报, 2020, 75(10): 2126-2145.

LI Ruzi, LIU Yaobin, WANG Wengang, XIE Dejin. China's urban land finance expansion and the transmission routes to economic efficiency[J]. Acta Geographica Sinica, 2020, 75(10): 2126-2145.

图/表 18

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表2

中国城市土地财政扩张对经济效率影响的基准回归结果①

解释变量	全国	东部地区	中部地区	西部地区	东北地区	超特大城市	Ⅰ型大城市	Ⅱ型大城市	中小城市
lnlandfinance	-0.00674^***	-0.00386	-0.0107^***	-0.00510^**	-0.0146^***	-0.000545	-0.00909^*	-0.00921^***	-0.00659^***
	(-4.57)	(-1.62)	(-3.28)	(-2.03)	(-2.97)	(-0.09)	(-1.70)	(-3.25)	(-3.55)
lnownership	0.0122^***	0.0264^***	0.00731^*	0.00629	0.00962	0.0351^**	0.00753	0.0141^***	0.0118^***
	(4.62)	(5.39)	(1.74)	(1.13)	(1.16)	(1.99)	(0.67)	(2.66)	(3.64)
lnstructure	0.00981^*	-0.000307	0.00705	-0.0172	0.0147	0.166^***	0.0308	0.0630^***	-0.00605
	(1.91)	(-0.03)	(0.66)	(-1.64)	(0.92)	(5.11)	(1.14)	(5.51)	(-0.97)
lnscifiscal	0.00912^***	0.00293	0.00589^*	0.0128^***	0.0149^***	0.00737	-0.00122	0.0134^***	0.00780^***
	(5.85)	(1.31)	(1.94)	(3.65)	(2.64)	(1.48)	(-0.25)	(5.06)	(3.63)
lnfiscal	-0.0381^***	-0.0428^***	-0.00714	-0.0406^***	-0.0154	-0.0258	-0.0447	-0.0213	-0.0364^***
	(-5.17)	(-2.89)	(-0.44)	(-3.54)	(-0.63)	(-0.81)	(-1.26)	(-1.44)	(-4.00)
lntrade	0.00543^**	0.0139^***	0.00517	0.00508	0.00428	0.0117	0.0368^*	0.00469	0.00478^*
	(2.58)	(2.87)	(1.27)	(1.48)	(0.64)	(1.25)	(1.82)	(0.88)	(1.95)
lnroaddensity	0.0837^***	0.114^***	0.105^***	0.0692^***	0.0338^***	0.169^***	0.161^***	0.0683^***	0.0876^***
	(18.93)	(15.24)	(13.20)	(7.27)	(2.72)	(7.29)	(7.26)	(9.15)	(15.43)
lnpubt	0.00555^*	0.0108^**	0.00557	-0.00800	-0.000244	0.0153	0.0179	0.00625	0.00415
	(1.74)	(2.31)	(1.04)	(-1.11)	(-0.02)	(0.66)	(1.11)	(0.88)	(1.10)
lngreen	0.000969	-0.00783	0.00487	0.00116	0.000556	-0.00615	0.0113	-0.00834	0.000761
	(0.31)	(-1.63)	(0.74)	(0.21)	(0.04)	(-0.47)	(0.84)	(-1.05)	(0.20)
lninternet	-0.0151^***	-0.0150^***	-0.00907	-0.0142^**	-0.0158	0.0119	-0.0139	-0.00100	-0.0235^***
	(-5.36)	(-4.32)	(-1.37)	(-2.32)	(-1.40)	(1.10)	(-1.61)	(-0.24)	(-5.55)
个体效应	是	是	是	是	是	是	是	是	是
时间趋势	是	是	是	是	是	是	是	是	是
常数项	17.54^***	14.38^***	27.52^***	16.25^***	10.58^**	35.97^***	14.46^*	22.98^***	15.20^***
	(10.83)	(5.15)	(7.88)	(4.93)	(2.00)	(6.25)	(1.92)	(7.54)	(6.71)
N	3058	952	876	856	374	132	163	666	2097
R²	0.364	0.426	0.490	0.328	0.237	0.563	0.455	0.447	0.357

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变量	地区	2005—2006年	2007—2008年	2009—2010年	2011—2012年	2013—2014年	2014—2015年
	全国	1.095	1.070	1.032	1.006	0.992	0.998
	东部地区	1.103	1.064	1.040	1.012	1.010	1.006
TFP	中部地区	1.085	1.063	1.021	0.992	0.973	0.977
	西部地区	1.102	1.077	1.034	1.009	0.987	1.001
	东北地区	1.081	1.085	1.031	1.015	1.000	1.020
	全国	1.045	1.102	1.024	1.027	1.030	1.007
	东部地区	1.053	1.104	1.027	1.036	1.035	1.015
TECH	中部地区	1.040	1.097	1.019	1.018	1.023	0.998
	西部地区	1.042	1.105	1.029	1.027	1.031	1.006
	东北地区	1.047	1.098	1.019	1.025	1.030	1.006
	全国	1.024	0.985	1.015	1.008	1.000	1.006
	东部地区	1.027	0.985	1.021	0.998	0.987	1.000
SECH	中部地区	1.016	0.989	1.010	1.007	0.994	1.003
	西部地区	1.032	0.985	1.014	1.017	1.020	1.014
	东北地区	1.017	0.977	1.011	1.014	0.995	1.009

解释变量	被解释变量：lnTECH
解释变量	全国	东部地区	中部地区	西部地区	东北地区	超特大城市	Ⅰ型大城市	Ⅱ型大城市	中小城市
lnlandfinance	-0.0122^***	-0.0138^***	-0.0149^***	-0.0111^***	-0.00833^***	-0.0182^**	-0.0169^***	-0.0147^***	-0.0117^***
	(-11.67)	(-6.70)	(-7.16)	(-6.39)	(-2.79)	(-2.29)	(-3.88)	(-6.12)	(-9.59)
控制变量	是	是	是	是	是	是	是	是	是
特征变量	是	是	是	是	是	是	是	是	是
个体效应	是	是	是	是	是	是	是	是	是
时间趋势	是	是	是	是	是	是	是	是	是
常数项	-0.00544^***	-0.00145	-0.00702^***	-0.00651^***	-0.00450^***	-0.00565	-0.00812^***	-0.00530^***	-0.00482^***
	(9.65)	(1.23)	(6.40)	(5.79)	(2.86)	(1.47)	(2.69)	(4.15)	(6.62)
N	3058	952	876	856	374	132	163	666	2097
R²	0.309	0.316	0.434	0.322	0.250	0.263	0.440	0.300	0.322

	lnhouseinvest	lnTFP	lnsalary	lnTFP	lnhouseprice	lnTFP
lnlandfinance	0.152^***		0.0173^***		0.0432^***
	(15.37)		(7.16)		(10.85)
lnhouseinvest		-0.0159^***
		(-5.86)
lnsalary				-0.0454^***
				(-3.94)
lnhouseprice						-0.0346^***
						(-5.01)
控制变量	是	是	是	是	是	是
特征变量	是	是	是	是	是	是
个体效应	是	是	是	是	是	是
时间趋势	是	是	是	是	是	是
常数项	-279.0^***	14.03^***	-211.7^***	9.217^***	-174.7^***	12.62^***
	(-25.67)	(7.80)	(-79.71)	(3.13)	(-39.96)	(6.21)
N	3058	3058	3058	3058	3058	3058
R²	0.862	0.367	0.970	0.363	0.904	0.365

	lnhouseinvest	lnTFP	lnsalary	lnTFP	lnhouseprice	lnTFP
lnlandfinance	0.125^***		0.0190^***		0.0869^***
	(8.55)		(4.39)		(12.58)
lnhouseinvest		-0.0279^***
		(-5.28)
lnsalary				-0.0972^***
				(-5.31)
lnhouseprice						-0.0196^*
						(-1.80)
控制变量	是	是	是	是	是	是
特征变量	是	是	是	是	是	是
个体效应	是	是	是	是	是	是
时间趋势	是	是	是	是	是	是
常数项	-294.5^***	6.226^*	-195.4^***	-4.279	-169.3^***	11.42^***
	(-17.22)	(1.95)	(-38.43)	(-0.94)	(-20.92)	(3.35)
N	952	952	952	952	952	952
R²	0.907	0.442	0.973	0.443	0.921	0.426

	lnhouseinvest	lnTFP	lnsalary	lnTFP	lnhouseprice	lnTFP
lnlandfinance	0.135^***		0.0219^***		0.0211^***
	(6.50)		(4.08)		(2.62)
lnhouseinvest		-0.0195^***
		(-3.57)
lnsalary				-0.00788
				(-0.36)
lnhouseprice						-0.0466^***
						(-3.22)
控制变量	是	是	是	是	是	是
特征变量	是	是	是	是	是	是
个体效应	是	是	是	是	是	是
时间趋势	是	是	是	是	是	是
常数项	-269.7^***	24.02^***	-212.8^***	28.13^***	-185.0^***	21.01^***
	(-12.13)	(6.34)	(-37.22)	(4.81)	(-21.53)	(4.79)
N	876	876	876	876	876	876
R²	0.893	0.491	0.974	0.483	0.928	0.490