Spatial-Temporal Evolution and Risk Assessment of Land Finance: Evidence from China
Abstract
:1. Introduction
2. An Overview of Land Finance in China
3. Methods and Data
3.1. Measurement of Land Finance
+ land value added tax + farmland occupation tax + deed tax
+ real estate related tax.
3.2. Average Growth Index
3.3. Global Moran’s I and Getis-Ord G
3.4. Composite Risk Index Model of Land Finance Risk
- Step 1. Constructing the assessment index system of land finance risk
- Step 2. Normalize the original data
- Step 3. Calculate the weight of each risk factor
- Step 4. Calculate the composite risk index
3.5. Grey Forecasting Model GM(1,1)
3.6. The SPR Model
3.7. Data and Software
4. Empirical Results
4.1. The General Tend of Land Finance
4.2. Spatial and Temporal Variation of Land Finance
4.3. Spatial Clustering Characteristic of Land Finance
4.4. Risk Assessment of Land Finance
4.5. The Key Risk Factors for Land Finance
4.6. Forecast of Land Finance Risks and Early Warnings
5. Policy Implications
6. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
1 | Generally, to scale variable into interval , we apply the formula where is the normalized value of (Basheera and Hajmeerb 2000). We normalize the original data into the interval to ensure the calculation of in the entropy method (Equation (15)) meaningful. |
2 | The province Xizang was excluded due to data availability. |
3 | http://www.ngcc.cn (accessd on 10 December 2021) |
4 | In March 2018, the Ministry of Natural Resources was established in accordance with the Institutional Reform Plan of the State Council approved at the first meeting of the 13th National People’s Congress; the responsibilities of the Ministry of Land and Resources were compiled into the newly established Ministry. The Ministry of Land and Resources was then no longer be retained. |
5 | The Notice on Regulating the State-Owned Land Use Rights Transfer Payments Management (The General Office of the State Council of the People’s Republic of China 2007). |
6 | The Notice on Improving the Level of Cultivated Land Protection and Comprehensively Strengthening the Construction and Management of Cultivated Land Quality (The Land Improvement Center of Ministry of Land and Resources 2012). |
7 | Hu Line was first identified by Dr. Huanyong Hu in 1935 (Hu 1935). |
8 | For more details in the three zones, see Table S3 in Supplementary Materials I. |
9 | Based on the LM test results reported in Table 4, we include only the spatial-fixed effects, but not the time-fixed effects in the fixed-effect regression. |
10 | We also investigate the models that include year dummy variables. The corresponding results from specifications [1]–[3] in Table 5 with year dummies are reported in Table S5 in Supplementary Materials I. Adding these year dummies does not affect the conclusion in risk assessment of land finance, except it slightly increases the of the fixed-effect model and significantly increases the of the random-effect model. |
11 | Due to the data availability, we conduct substage regressions based only on data of 2009–2016 (Stages 2 and 3). |
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Type (C) | Risk Variable (X—Normalized Variable) | Variable Description | Expected Sign | |
---|---|---|---|---|
Social risks | X1 | Urbanization rate (%) | The proportion of the population living in urban areas. It represents the urbanization level of a country or region. | + |
X2 | Increase of developed land (hm2) | The amount of agricultural land and unused land converts to developed land per year. | + | |
X3 | Average housing price growth rate (%) | The growth rate in year t equals the average price of commodity house in year t minus the average price of commodity house in year t − 1 divided by the average price of commodity house in year t − 1. | + | |
X4 | The income disparity between urban and rural residents (%) | The ratio of the disposable income of urban residents per capita to the net income of rural residents per capita. The ratio represents the risk of income differentiation between urban and rural residents. | + | |
X5 | Degree of development of land markets (%) | The proportion of the total amount of land transferred through bidding, auction, and sale, representing the degree of development of land markets. | − | |
X6 | Cultivated land per capita (hm2/capita) | Cultivated land area/total population, representing cultivated area per capita. | − | |
Economic risks | X7 | Degree of land transfer dependence (%) | The proportion of land finance scaled by local revenue. It represents the dependence of the local government on land finance. | + |
X8 | Deficit ratio (%) | The proportion of fiscal deficit in GDP, representing the comparison of the annual revenue and expenditure of local governments. | + | |
X9 | Macro tax burden (%) | The proportion of government revenue in GDP, representing a country’s overall level of the tax burden and overall economic strength. | + | |
X10 | The asset-liability ratio of real estate enterprises (%) | The ratio of total ending liabilities to total assets, representing the ability of an enterprise to conduct business activities with funds provided by creditors. | + | |
Legal risks | X11 | Number of land violation cases (#) | It measures the level of corruption of local government in land management. | + |
X12 | Number of illegal land occupation cases (#) | It measures the management level of local government in the field of land law enforcement and supervision. | + | |
X13 | Percentage of cultivated land in illegal occupation cases (%) | It measures the extent of damage to cultivated land resources caused by local government’s illegal occupation. | + | |
Ecological risks | X14 | Percentage of cultivated land area (%) | The proportion of cultivated land in total land area. | − |
X15 | Cultivated land decrease rate (%) | The ratio of the annual decrease in cultivated land to the total cultivated land, representing the extent of damage of annual cultivated land. | − |
Accuracy Level | Small Error Probability (p) | Posterior Error Ratio (c) |
---|---|---|
Good | p > 0.95 | < 0.35 |
Qualified | 0.8 < p ≤ 0.95 | < 0.5 |
Barely qualified | 0.7 < p ≤ 0.8 | < 0.65 |
Fail | p ≤ 0.7 |
Stages | Year | Moran’s I | p-Value | Getis-Ord G | p-Value | ||
---|---|---|---|---|---|---|---|
First stage (2001–2008) | 2001 | 0.2324 | 2.3532 | 0.0220 | 0.2052 | 2.1001 | 0.0357 |
2002 | 0.3835 | 3.8478 | 0.0050 | 0.2454 | 3.1507 | 0.0016 | |
2003 | 0.3786 | 3.9569 | 0.0050 | 0.2516 | 3.0904 | 0.0020 | |
2004 | 0.4436 | 3.9794 | 0.0020 | 0.1937 | 2.3292 | 0.0198 | |
2005 | 0.4357 | 4.1799 | 0.0010 | 0.2314 | 3.1887 | 0.0014 | |
2006 | 0.3790 | 3.5902 | 0.0040 | 0.2176 | 3.0099 | 0.0026 | |
2007 | 0.2386 | 2.3183 | 0.0220 | 0.2008 | 2.4984 | 0.0125 | |
2008 | 0.3365 | 3.1345 | 0.0030 | 0.1921 | 2.4350 | 0.0149 | |
Second stage (2009–2012) | 2009 | 0.3669 | 3.5018 | 0.0040 | 0.2169 | 2.7887 | 0.0053 |
2010 | 0.3410 | 3.2383 | 0.0040 | 0.2071 | 2.8024 | 0.0051 | |
2011 | 0.2696 | 2.6398 | 0.0090 | 0.1876 | 2.3521 | 0.0187 | |
2012 | 0.2598 | 2.5950 | 0.0110 | 0.1947 | 2.8730 | 0.0041 | |
Third stage (2013–2016) | 2013 | 0.2806 | 2.7945 | 0.0060 | 0.2023 | 3.0680 | 0.0022 |
2014 | 0.2748 | 2.7005 | 0.0110 | 0.1933 | 2.8582 | 0.0043 | |
2015 | 0.2858 | 2.9026 | 0.0060 | 0.1997 | 2.9262 | 0.0034 | |
2016 | 0.3686 | 3.7056 | 0.0030 | 0.2251 | 3.5199 | 0.0004 |
Determinants | Statistics | Determinants | Statistics |
---|---|---|---|
LM test spatial error | 7.444 *** | LM test spatial lag | 3.203 * |
Robust test LM spatial error | 4.468 ** | Robust LM test spatial lag | 0.226 |
Hausman test | Negative |
Basic Level Indicators (X—Normalized Variable) | Fixed-Effects [1] | Random-Effects [2] | Random-Effects [3] | |
---|---|---|---|---|
X1 | Urbanization rate | 0.99 *** | 0.4642 *** | 0.4563 *** |
(0.139) | (3.4856) | (5.3473) | ||
X2 | Increase of developed land | 0.12 *** | 0.1483 *** | 0.1552 *** |
(0.029) | (4.4383) | (4.9131) | ||
X3 | Average housing price growth rate | 0.03 | −0.0111 | |
(0.026) | (−0.3778) | |||
X4 | The income disparity between urban & rural residents | 0.16 *** | 0.052 | |
(0.056) | (0.8732) | |||
X5 | Degree of land market development | −0.09 *** | −0.0928 *** | −0.0854 ** |
(0.035) | (−2.6519) | (−2.5372) | ||
X6 | Per capita cultivated land | 1.32 * | 0.0906 | |
(0.795) | (0.8169) | |||
X7 | Degree of land transfer dependence | 0.29 *** | 0.2669 *** | 0.2606 *** |
(0.028) | (8.854) | (9.2761) | ||
X8 | Deficit rate | −0.11 | −0.0305 | |
(0.116) | (−0.3144) | |||
X9 | Macro tax burden | 0.39 *** | 0.2264 *** | 0.2291 *** |
(0.073) | (2.8354) | (2.9582) | ||
X10 | The asset-liability ratio of real estate enterprises | −0.00 | 0.0745 * | |
(0.042) | (1.6478) | |||
X11 | Number of land violation cases | 0.02 | 0.0038 | |
(0.028) | (0.1212) | |||
X12 | Number of illegal land occupation cases | 0.08 ** | 0.0864 ** | 0.0808 ** |
(0.034) | (2.2771) | (2.4077) | ||
X13 | Percentage of cultivated land in illegal occupation cases | −0.04 | −0.0338 | |
(0.022) | (−1.3608) | |||
X14 | Percentage of cultivated land area | −2.39 * | 0.1231 | |
(1.319) | (1.2962) | |||
X15 | Cultivated land decrease rate | −0.07 * | −0.0419 | |
(0.036) | (−1.0392) | |||
Constant | −0.3173 *** | −0.2109 *** | ||
(−2.7230) | (−5.4626) | |||
) | 0.23 *** | 0.3211 *** | 0.3053 *** | |
(0.056) | −5.6324 | (5.5732) | ||
0.151 | 0.461 | 0.441 |
Variables | Direct Effects | Indirect Effects | Total Effects |
---|---|---|---|
X1 | 0.4826 *** | 0.2120 *** | 0.6946 *** |
(3.4504) | (2.8132) | (3.4517) | |
X2 | 0.1511 *** | 0.0677 *** | 0.2188 *** |
(4.4625) | (2.6900) | (3.9534) | |
X3 | −0.0082 | −0.0036 | −0.0118 |
(−0.2829) | (−0.2657) | (−0.2796) | |
X4 | 0.0535 | 0.0248 | 0.0783 |
(0.9086) | (0.8819) | (0.9093) | |
X5 | −0.0943 *** | −0.0418 ** | −0.1361 *** |
(−2.6796) | (−2.2177) | (−2.6230) | |
X6 | 0.1010 | 0.0443 | 0.1453 |
(0.8733) | (0.8212) | (0.8661) | |
X7 | 0.2746 *** | 0.1218 *** | 0.3965 *** |
(8.4662) | (3.6707) | (7.0936) | |
X8 | −0.0360 | −0.0157 | −0.0518 |
(−0.3618) | (−0.3597) | (−0.3638) | |
X9 | 0.2421 *** | 0.1046 *** | 0.3467 *** |
(3.0106) | (2.8163) | (3.1360) | |
X10 | 0.0771 | 0.0344 | 0.1115 |
(1.6440) | (1.5054) | (1.6328) | |
X11 | 0.0031 | 0.0008 | 0.0039 |
(0.0980) | (0.0569) | (0.0856) | |
X12 | 0.0913 ** | 0.0414 * | 0.1327 ** |
(2.1911) | (1.7745) | (2.0949) | |
X13 | −0.0358 | −0.0160 | −0.0518 |
(−1.4555) | (−1.3470) | (−1.4434) | |
X14 | 0.1309 | 0.0584 | 0.1892 |
(1.3166) | (1.2162) | (1.3045) | |
X15 | −0.0466 | −0.0199 | −0.0665 |
(−1.1569) | (−1.0998) | (−1.1544) | |
0.4613 | 0.4613 | 0.4613 |
Basic Level Indicators (X) | Zone 1 | Zone 2 | Zone 3 | Stage 2 | Stage 3 |
---|---|---|---|---|---|
(Western) | (Central) | (Eastern coastal) | (2009–2012) | (2013–2016) | |
X1 | 0.0743 | 0.7820 *** | 2.4984 *** | 0.1591 | 0.4986 *** |
(1.5280) | (5.6217) | (5.9812) | (1.3943) | (3.8610) | |
X2 | −0.0067 | 0.0564 ** | 0.0569 | 0.1250 *** | 0.1128 *** |
(−0.4440) | (2.0638) | (1.0797) | (3.4127) | (2.5767) | |
X3 | −0.0114 | 0.0758 *** | 0.0735 | −0.0361 | 0.0604 |
(−1.2663) | (2.8920) | (0.9658) | (−1.3251) | (1.4918) | |
X4 | −0.0638 *** | 0.1153 * | −0.2369 | −0.0401 | −0.0616 |
(−3.3762) | (1.9566) | (−0.7532) | (−0.4882) | (−0.3569) | |
X5 | 0.0238 ** | 0.0880 *** | −0.5373 *** | −0.0266 | 0.0586 |
(2.0121) | (2.6426) | (−3.5224) | (−0.6582) | (1.4548) | |
X6 | 0.0002 | −3.0799 *** | 15.3790 *** | −0.0410 | −0.0686 |
(0.0038) | (−2.6226) | (4.7366) | (−0.5715) | (−0.5883) | |
X7 | 0.1516 *** | 0.3360 *** | 0.6436 *** | 0.2193 *** | 0.4353 *** |
(14.1764) | (13.6490) | (8.2513) | (6.4781) | (9.8544) | |
X8 | 0.0005 | −0.2254 | 0.1196 | −0.0996 | −0.0217 |
(0.0162) | (−1.3303) | (0.1605) | (−1.1050) | (−0.2103) | |
X9 | 0.0861*** | 0.5962 *** | 1.0444 *** | 0.0114 | 0.0941 |
(3.7270) | (8.8572) | (4.3906) | (0.1177) | (1.1225) | |
X10 | −0.0112 | −0.1004 ** | 0.2981 | 0.0770 * | 0.1311 ** |
(−0.9659) | (−2.3699) | (1.2947) | (1.8345) | (1.9899) | |
X11 | −0.0037 | −0.0163 | −0.0064 | −0.0120 | −0.0164 |
(−0.2799) | (−0.8015) | (−0.0922) | (−0.1851) | (−0.5081) | |
X12 | −0.0094 | 0.0056 | −0.0176 | −0.0564 | 0.0408 |
(−0.4266) | (0.1806) | (−0.2761) | (−0.7179) | (1.3165) | |
X13 | 0.0065 | −0.0138 | −0.0535 | 0.0137 | −0.0247 |
(0.8757) | (−0.7090) | (−0.8270) | (0.4421) | (−0.9511) | |
X14 | −0.0282 | −0.0862 | −1.2151 *** | 0.0261 | 0.0929 |
(−0.4866) | (−0.1481) | (−3.2706) | (0.3771) | (0.8823) | |
X15 | −0.0045 | −0.1000 ** | −0.1621 | 0.0178 | −0.2839 |
(−0.4319) | (−2.0330) | (−1.0324) | (0.5112) | (−0.3991) | |
Constant | 0.0774 * | 0.3500 | −3.1251 *** | 0.0071 | −0.1440 |
(1.8369) | (0.8631) | (−5.1530) | (0.0621) | (−0.3150) | |
Spatial weights (ρ) | 0.1501 ** | 0.1464 ** | −0.0982 | 0.2562 *** | 0.0536 |
(2.3648) | (2.0125) | (−1.0179) | (2.7880) | (0.6676) | |
0.085 | 0.006 | 0.715 | 0.659 | 0.561 |
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Zhou, D.; Tian, R.; Lin, Z.; Liu, L.; Wang, J.; Feng, S. Spatial-Temporal Evolution and Risk Assessment of Land Finance: Evidence from China. Risks 2022, 10, 196. https://doi.org/10.3390/risks10100196
Zhou D, Tian R, Lin Z, Liu L, Wang J, Feng S. Spatial-Temporal Evolution and Risk Assessment of Land Finance: Evidence from China. Risks. 2022; 10(10):196. https://doi.org/10.3390/risks10100196
Chicago/Turabian StyleZhou, De, Ruilin Tian, Zhulu Lin, Liming Liu, Junfeng Wang, and Shijia Feng. 2022. "Spatial-Temporal Evolution and Risk Assessment of Land Finance: Evidence from China" Risks 10, no. 10: 196. https://doi.org/10.3390/risks10100196