Evaluating the Impact of County-to-District Transformation on Urban Residential Land Supply: A Multi-Period Difference-in-Differences Model Analysis
Abstract
:1. Introduction
2. Policy Background and Theoretical Analysis
2.1. Policy Background
- (1)
- Initial exploration of CDT (1983–1997): During the early stages of China’s reform and opening up period, the primary focus of administrative restructuring was on transforming prefectures into prefecture-level cities and counties into county-level cities [30]. It was not until 1983 that cities began experimenting with the implementation of CDT. For over a decade, the practice of this phenomenon in China was sporadic until 1997, with fewer than 30 cases existing. Many were primarily within the eastern region (12 cases) and within cities of higher administrative levels and larger scales, such as sub-provincial cities and municipalities directly under the central government’s control.
- (2)
- First peak in CDT (1998–2004): Due to the conflict between the conversion of counties to county-level cities and urbanization demands, CDT approval was suspended in 1997. Concurrently, local governments sought to increase fiscal revenue through urban land expansion following the tax-sharing reform in 1994. As a result, CDT adoption increased significantly, with 48 cases reported between 1998 and 2004. Although examples remained concentrated in economically developed eastern coastal areas (33 cases), they also occurred in several different types of cities and were no longer exclusive to directly controlled municipalities or provincial capitals. The number of cases in general prefecture-level cities rose to 24.
- (3)
- Postponement period of CDT (2005–2009): Due to the central government’s tightened approval process, CDT reform entered a cooling-off period. During this time, CDT cases declined substantially, with only five cities (Baishan in Jilin Province, Harbin in Heilongjiang Province, Chongqing Municipality, Urumqi in Xinjiang Uygur Autonomous Region, and Nantong in Jiangsu Province) implementing the practice.
- (4)
- Second peak in CDT (2010–2017): In 2009, the Ministry of Finance actively promoted the implementation of provincially administered county reform, prompting local governments to eagerly pursue CDT to prevent the detachment and uncontrolled management of counties within their jurisdictions. There were 91 cases of CDT distributed across the eastern (43 cases), central (12 cases), western (31 cases), and northeastern (5 cases) regions. Compared to the first peak, the number of CDT examples during this stage increased significantly overall, and inter-regional differences gradually diminished. From an administrative perspective, the number of cases in general-level cities rapidly increased, reaching 60 cases and accounting for 65.3% of the total during this stage. This trend suggests that general-level cities have actively sought opportunities to expand urban development space and enhance economic development scale, leading to the continued emergence of CDT practices.
2.2. Theoretical Analysis
- (1)
- Functional transformation effect: On the one hand, as CDT is implemented, and counties are progressively integrated into the comprehensive urban development plan as part of the central urban area, they hold the potential to develop as new commercial and residential hubs for the entire city. This may lead to an expansion of the residential land supply within the original county area. On the other hand, due to the expansion of built-up areas, many cities adopt or transition toward a large-city model, which transforms spatial functions, particularly the growth and extension of residential and service areas based on the initial plan. Consequently, the city government will inevitably intensify residential construction and public facilities investment [19], leading to a substantial increase in the proportion of residential land supply for the entire city.
- (2)
- Income-boosting effect: Generally, before CDT, and influenced by factors such as location and size, counties primarily focused on developing urban support industries and agriculture. As they gradually become part of the central urban area and infrastructure improves, they will not only prioritize acquiring related industries by relocating from the central city, but they will also serve as an incubator for emerging industries; thus, these areas foster the enhancement and optimization of the city’s entire industry [31]. This will contribute to the city’s overall economic development and significantly increase the residents’ income. It is plausible that the rise in urban residents’ income will result in heightened demand for housing, particularly high-quality housing. Consequently, this will encourage the government to expand the residential land supply scale, leading to a price increase in residential land due to the heightened demand for high-quality accommodation.
- (3)
- Scale-expansion effect: This effect is primarily associated with the agglomeration of the population and the labor force, and the impact of CDT on the urban populace and workers can be observed in the following three situations. First, the expansion of urban areas and investment in supporting infrastructure attracts more people to migrate and gather, especially those from outside the area [32]. Second, CDT is a catalyst for urban-based industry upgrading and optimization, particularly the rapid development of commercial and service industries, which significantly promotes the introduction and concentration of the labor force. Third, the development of the urban economy and the improvement in residents’ income after CDT also helps curb the outflow of the population and workers [33]. Each of these situations may increase the demand for housing in the entire city to varying degrees, leading to an expansion in the supply scale and proportion of residential land.
3. Model and Data
3.1. Multi-Period DID
3.2. Data, Variables, and Descriptive Statistics
- (1)
- Dependent variables: As the Ministry of Land and Resources (currently the Ministry of Natural Resources) generally selects indicators, such as supply scale, structure, and price for compiling and summarizing information on urban land supply, this study employed three indicators of its own: residential land supply area (Supply, unit: hm2), the proportion of residential land supply (Prop, the ratio of residential land supply to total land supply, unit: %), and residential land price (Price, unit: RMB yuan/m2) to reflect the situation and changes in urban residential land supply. The first two data sets were obtained from China Land and Resources Statistical Yearbook, which collected panel data for 285 prefecture-level cities in China. The residential land price data were sourced from the China Land Value Information Service Platform (www.landvalue.com.cn, accessed on 23 December 2020), which includes monitored information on land prices in 105 cities, such as municipalities directly under the central government’s jurisdiction, provincial capital cities, and cities specifically designated in the state plan. Given the level of data availability, this paper primarily investigated the impact of CDT policy on the overall residential land supply of prefecture-level cities.
- (2)
- Independent variable: To ensure data typicality and availability and to exclude interference caused by the first wave of policy implementation, this study selected the timespan from 2009 to 2017 as the research period 4. The key explanatory variable is the CDT policy (Reform, which is designated according to the relevant discussions in Section 3.1). It is important to note that the policy-related research object of this study refers to the practice of local governments actively applying for and obtaining central government approval for the CDT to expand the urban spatial scale. However, it does not include CDT practices indirectly caused by other administrative adjustments, such as converting prefectures to prefecture-level cities and counties to county-level cities [36]. This study pre-processed the samples as follows: First, four special municipalities with independent administrative status, namely, Beijing, Shanghai, Chongqing, and Tianjin, were excluded. Second, samples that had experienced this transformation at least once between 2005 and 2008 or two or more times during the sample period were excluded to avoid the mutual interference of policy practices in different timespans and to concentrate on the dynamic effects of CDT policy. Third, samples with significant data missing, including some prefecture-level cities in the Xinjiang Uygur Autonomous Region, Qinghai Province, and Tibet Autonomous Region, were excluded. After the screening, a total of 264 prefecture-level city samples were obtained, of which 79 prefecture-level cities with observable residential land prices were selected for analysis with regard to the impact of CDT on residential land prices.
- (3)
- Control variables: First, economic development indicators were selected. Economic growth can directly stimulate the demand for residential land [37]. Consequently, gross regional product (Gdp, unit: RMB ten thousand yuan) and fiscal expenditure (Gov, unit: RMB ten thousand yuan) were utilized to reflect the degree of urban economic development. Second, since the development of the real estate industry is a crucial factor in influencing the supply of residential land [38], the proportion of real estate investment to fixed asset investment (Inv, unit: %) was employed to characterize the activity of real estate investment. Third, as the adjustment and upgrading of the industrial structure will inevitably lead to the reorganization of construction land across different sectors [39], the ratio of secondary and tertiary industries (Is, unit: %) was used to reflect changes in the urban industrial structure. Finally, areas with high population densities have a relatively greater demand for residential land [40], so population density (Pd, unit: persons/km2) was used to measure the urban population. The data for the aforementioned control variables were sourced from the China City Statistical Yearbook, the China Stock Market and Accounting Research Database (https://www.gtarsc.com/, accessed on 23 December 2020), and various provincial and municipal statistical bulletins.
4. Results and Discussion
4.1. Results of the Baseline Model
4.2. Parallel Trend Test
4.3. Robustness Test
4.3.1. Difference-in-Differences after Propensity Score Matching (PSM-DID)
4.3.2. Placebo Test
4.3.3. Policy Spillover Effect Test
4.4. Results of Heterogeneous Regression
4.5. Mechanism Analysis
5. Conclusions and Recommendations
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
1 | Strictly speaking, the term “county” include county-level cities in this context and the phrasing should be “county (or county-level city)-to-district transformation.” For the sake of brevity, however, this paper shall adopt the shorthand “county-to-district transformation.” |
2 | The urban hierarchy in China can be generally classified into three levels. The first level consists of municipalities directly under the jurisdiction of central government, including Beijing, Tianjin, Shanghai, and Chongqing, which concurrently govern multiple districts and counties (or county-level cities). The second level comprises prefecture-level cities, which also govern several districts or counties (or county-level cities), including some sub-provincial cities and regular prefecture-level cities. The third level consists of county-level cities, which are governed by prefecture-level cities and are administratively unified with the districts under their jurisdiction. |
3 | In the following text, the number of CDT cases in different regions, periods, and degrees of cities is obtained through the statistical analysis of relevant data on China’s administrative divisions website. |
4 | First, in terms of the stages of CDT practice, this study primarily focused on the second peak of CDT (2010–2017). Compared to the first peak (1998–2004), this period has more policy practice samples, and the distribution of sample cities’ locations and scales is more balanced, making the samples more representative and comprehensive. Second, to reduce estimation bias caused by policy expectations (the impact of the first peak of CDT on the current period), the threshold of this policy should be in or after 2005. Finally, due to data availability (the monitoring range of national land prices was only upgraded to 105 cities since the fourth quarter of 2008, and the relevant statistical yearbooks have not published the total supply and proportion of residential land supply in each city after 2017), this paper ultimately selected 2009 as the pre-treatment point and 2017 as the post-treatment point. |
5 | To maintain the validity of the regression results, we did not include the types of cities with a small number of samples in the empirical analysis-mega-cities and super large cities. |
6 | The data of the aforementioned dependent variables were sourced from the China City Statistical Yearbook. Additionally, a logarithmic transformation was applied to the continuous variables (excluding those measured in proportion) during the model estimation. |
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Variable | Implication | Observations | Mean | Std. Dev. | Min | Max | |
---|---|---|---|---|---|---|---|
Dependent variable | lnSupply | Residential land supply total, take logarithm value | 2376 | 5.320 | 0.935 | 2.754 | 7.373 |
Prop | Proportion of residential land supply | 2376 | 0.205 | 0.107 | 0.022 | 0.513 | |
lnPrice | Residential land price, take logarithm value | 711 | 7.627 | 0.831 | 5.361 | 10.734 | |
Independent variable (full sample) | Reform | Whether to implement the policy practice | 2376 | 0.103 | 0.304 | 0 | 1 |
Independent variable (samples of key monitored cities for land prices) | Reform | Whether to implement the policy practice | 711 | 0.142 | 0.349 | 0 | 1 |
Control variable (full sample) | lnGdp | Gross regional product, take logarithm | 2376 | 16.316 | 0.850 | 14.445 | 18.405 |
Inv | Real estate as a percentage of fixed investment | 2376 | 0.147 | 0.089 | 0.022 | 0.441 | |
lnGov | Fiscal expenditure, take logarithm | 2376 | 14.545 | 0.666 | 12.972 | 16.285 | |
Is | Industrial structure | 2376 | 0.869 | 0.078 | 0.545 | 0.997 | |
lnPd | Population density, take logarithm | 2376 | 5.709 | 0.905 | 1.603 | 7.882 | |
Control variable (samples of key monitored cities for land prices) | lnGdp | Gross regional product, take logarithm | 711 | 16.923 | 0.840 | 14.445 | 18.405 |
Inv | Real estate as a percentage of fixed investment | 711 | 0.186 | 0.099 | 0.032 | 0.671 | |
lnGov | Fiscal expenditure, take logarithm | 711 | 14.916 | 0.707 | 12.874 | 16.900 | |
Is | Industrial structure | 711 | 0.899 | 0.077 | 0.584 | 0.997 | |
lnPd | Population density, take logarithm | 711 | 6.133 | 0.777 | 3.657 | 7.825 |
Dependent Variable | lnSupply | lnProp | lnPrice | lnSupply | lnProp | lnPrice |
---|---|---|---|---|---|---|
Independent Variable | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 |
Reform | 0.141 *** | 0.013 * | 0.081 ** | 0.142 *** | 0.015 * | 0.064 * |
(0.044) | (0.008) | (0.037) | (0.043) | (0.008) | (0.036) | |
lnGdp | 0.491 ** | −0.001 | 0.093 | |||
(0.204) | (0.015) | (0.106) | ||||
Inv | 0.882 *** | 0.100 * | 1.021 *** | |||
(0.284) | (0.056) | (0.197) | ||||
lnGov | 0.823 *** | 0.033 ** | 0.185 ** | |||
(0.157) | (0.015) | (0.075) | ||||
Is | 2.997 *** | 0.147 | 0.856 | |||
(0.995) | (0.139) | (0.664) | ||||
lnPd | 0.193 | −0.032 | 0.644 *** | |||
(0.289) | (0.052) | (0.203) | ||||
Control variable | NO | NO | NO | YES | YES | YES |
Individual fixed effect | YES | YES | YES | YES | YES | YES |
Time fixed effect | YES | YES | YES | YES | YES | YES |
Constant term | 5.306 *** | 0.203 *** | 7.613 *** | −18.510 *** | −0.221 | −1.611 |
(0.011) | (0.000) | (0.009) | (2.915) | (0.398) | (1.664) | |
Observations | 2376 | 2376 | 711 | 2376 | 2376 | 711 |
R2 | 0.757 | 0.416 | 0.954 | 0.783 | 0.419 | 0.960 |
Dependent Variable | lnSupply | lnProp | lnPrice | lnSupply | lnProp | lnPrice |
---|---|---|---|---|---|---|
Independent Variable | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 |
Reform | 0.151 *** | 0.014 * | 0.074 ** | 0.152 *** | 0.016 * | 0.066 * |
(0.045) | (0.008) | (0.037) | (0.044) | (0.008) | (0.038) | |
Control variable | NO | NO | NO | YES | YES | YES |
Individual fixed effect | YES | YES | YES | YES | YES | YES |
Time fixed effect | YES | YES | YES | YES | YES | YES |
Observations | 2291 | 2291 | 686 | 2291 | 2291 | 685 |
R2 | 0.758 | 0.424 | 0.956 | 0.785 | 0.427 | 0.960 |
Dependent Variable | lnSupply | lnProp | lnSupply | lnProp |
---|---|---|---|---|
Independent Variable | Model 1 | Model 2 | Model 3 | Model 4 |
NR | −0.039 | −0.011 | −0.054 | −0.013 |
(0.053) | (0.008) | (0.047) | (0.008) | |
Control variable | NO | NO | YES | YES |
Individual fixed effect | YES | YES | YES | YES |
Time fixed effect | YES | YES | YES | YES |
Observations | 1784 | 1784 | 1784 | 1784 |
R2 | 0.703 | 0.402 | 0.742 | 0.703 |
lnSupply | lnProp | lnPrice | ||||||
---|---|---|---|---|---|---|---|---|
City Type | Small City | Medium City | Type-II Large City | Small City | Medium City | Type-II Large City | Medium City | Type-II Large City |
Reform | 0.130 | 0.160 ** | 0.262 *** | −0.003 | 0.017 | 0.031 ** | 0.006 | 0.092 ** |
(0.093) | (0.071) | (0.067) | (0.019) | (0.014) | (0.013) | (0.104) | (0.044) | |
Control variable | YES | YES | YES | YES | YES | YES | YES | YES |
Individual fixed effect | YES | YES | YES | YES | YES | YES | YES | YES |
Time fixed effect | YES | YES | YES | YES | YES | YES | YES | YES |
Observations | 936 | 837 | 513 | 936 | 837 | 513 | 279 | 324 |
R2 | 0.692 | 0.720 | 0.772 | 0.415 | 0.390 | 0.460 | 0.930 | 0.964 |
Dependent Variable | lnBua | Pdr | lnNe | lnPtw |
---|---|---|---|---|
Independent Variable | Model 1 | Model 2 | Model 3 | Model 4 |
Reform | 0.147 *** | 0.327 ** | 0.034 ** | 0.035 |
(0.019) | (0.166) | (0.015) | (0.028) | |
Control variable | YES | YES | YES | YES |
Individual fixed effect | YES | YES | YES | YES |
Time fixed effect | YES | YES | YES | YES |
Constant term | 1.859 *** | 91.790 *** | −1.500 * | 10.140 *** |
(0.525) | (4.733) | (0.887) | (0.317) | |
Observations | 2376 | 2376 | 2112 | 2292 |
R2 | 0.941 | 0.177 | 0.957 | 0.301 |
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Zhen, M.; Yu, J.; Chen, S.; Wang, N.; Chen, Z. Evaluating the Impact of County-to-District Transformation on Urban Residential Land Supply: A Multi-Period Difference-in-Differences Model Analysis. Land 2023, 12, 1149. https://doi.org/10.3390/land12061149
Zhen M, Yu J, Chen S, Wang N, Chen Z. Evaluating the Impact of County-to-District Transformation on Urban Residential Land Supply: A Multi-Period Difference-in-Differences Model Analysis. Land. 2023; 12(6):1149. https://doi.org/10.3390/land12061149
Chicago/Turabian StyleZhen, Mengjia, Junlan Yu, Siyi Chen, Ning Wang, and Zhigang Chen. 2023. "Evaluating the Impact of County-to-District Transformation on Urban Residential Land Supply: A Multi-Period Difference-in-Differences Model Analysis" Land 12, no. 6: 1149. https://doi.org/10.3390/land12061149
APA StyleZhen, M., Yu, J., Chen, S., Wang, N., & Chen, Z. (2023). Evaluating the Impact of County-to-District Transformation on Urban Residential Land Supply: A Multi-Period Difference-in-Differences Model Analysis. Land, 12(6), 1149. https://doi.org/10.3390/land12061149