How Do Institutional Arrangements Affect Corporate Social Mobility? Evidence from Construction Land Reduction

Abstract

Enhancing corporate social mobility is of great practical importance for improving economic dynamism. There are new institutional arrangements in place to optimize construction land structure, i.e., construction land reduction. The impact of institutional arrangements on corporate social mobility has not yet been explored in academia. There is also a lack of academic discussion on how to enhance corporate social mobility. This paper investigates the impact of institutional arrangements on corporate social mobility using construction land reduction as an example. The following findings are discussed: (1) Construction land reduction is an important institutional arrangement for solving problems of inefficiencies such as inefficient corporates, the transfer of inefficient construction land, spatial quotas’ allocation, and macro use conversion. (2) Construction land reduction significantly promotes corporate social mobility. (3) Compared to non-cadre residents, cadres perceive greater corporate social mobility. (4) Compared to planned incremental-type areas, planned decremental-type areas are the key areas for construction land reduction, which can free up more space for construction land and is more conducive to improving corporate social mobility. Accordingly, policy implications are proposed to improve construction land reduction policies and promote corporate social mobility.

1. Introduction

In 2020, China’s total carbon emission share among the world’s major countries is still growing [1]. There are many institutional arrangements in place to achieve carbon peaking and carbon neutrality, and construction land reduction is one said way (please refer to Appendix A for a visual understanding of construction land reduction). The above-mentioned method is positive to promote carbon peaking and carbon neutrality from the factor-supply side. By reducing inefficient construction land and using the reduced construction land in more efficient places, it can promote high-quality economic development with no increase in construction land and a subsequent reduction in carbon emissions. Thus, construction land reduction can not only achieve the spatial optimization of land structure, but also create conditions for carbon peaking.

Shanghai is the first city in China to carry out construction land reduction to explore methods for high-quality land use [2,3,4]. Construction land reduction, converting construction land into non-construction land, has become a way to optimize land use in economically developed regions [2]. Construction land reduction is a mechanism for realizing land factors’ flow and promoting the optimization of construction land’s spatial layout [2,4].

Economic development is driven by factor inputs [5,6,7,8], out of which land inputs are the most fundamental. China’s economic development has long relied excessively on factor input growth, which is a typical factor-driven development model. Rapid urbanization has led to the continuous expansion of China’s built-up area [9], and construction land contradiction has become increasingly prominent. At this stage, China strictly controls the construction land [10], and the high-quality use of land resources is an inevitable trend.

Local governments face the challenge of insufficient construction land for economic development [11]. In economically developed areas, construction land is severely scarce [4,12]. On the one hand, the supply of new construction land is insufficient; on the other hand, the stock of construction land is inefficiently allocated. This is manifested as follows: (1) The layout of the stock of inefficient corporates is scattered, and the corporates exceed the actual scale of demand for land, resulting in an inefficient and excessive use of land. (2) Due to the poor quality of the corporates themselves, improving quality and efficiency is difficult, meaning that the corporates’ inefficient use of the status quo is difficult to change, resulting in inefficiency problems such as those of the inefficient corporates themselves. (3) A difficulty in transferring the property rights of construction land can lead to inefficiencies in the transfer of inefficient construction land. These inefficiency problems have restricted the social mobility of corporates. Land, as the most fundamental factor input, is an important influencing factor of corporate social mobility. Revitalizing stock construction land and raising the access standards for new construction land are key to improving the efficiency of land resource allocation [4].

Economically developed regions have explored institutional arrangements that address the demand for construction land through the mobility of land factors, which, in turn, provide construction land space for new corporates. Through the reduction in inefficient construction land in distant suburban areas, space is provided for the entry of new corporates in suburban areas. Thus, the question arises: how do institutional arrangements affect corporate social mobility? Is corporate social mobility affected by the heterogeneity of land use planning? The answers to these questions have important reference value for enhancing corporate social mobility. The innovations of this paper include the following: (1) empirically investigating the impact of institutional arrangements on corporate social mobility using construction land reduction as an example, based on the analysis of theoretical mechanisms; (2) analyzing the heterogeneous impact of land-use planning on corporate social mobility; and (3) proposing policy recommendations for enhancing corporate social mobility. A glossary of the terms used in this paper is attached as Appendix B.

The rest of the paper is structured as follows: part two covers the theoretical mechanisms and research hypotheses; part three outlines the research design and data sources; part four discusses the empirical results and analysis; part five comprises the discussion; and, finally, the conclusions and policy implications are summarized.

2. Theoretical Mechanisms and Research Hypotheses

2.1. Corporate Social Mobility and Its Manifestations

2.1.1. Corporate Social Mobility

Factors of production have a certain hierarchy. Land factors are the basic and load-bearing factors of urban development [13]. The factors of production are carried by certain land factors. Capital buys liquid factors of production and corporates organize their employees for labor services’ production. The increase in the mobility of land factors has a fundamental role, and the allocation of land factors among different corporates drives the increase in corporate social mobility. The mobility of land factors brought about by construction land reduction is a fundamental condition for enhancing the dynamism of the economy and facilitating the mobility of other factors. Social mobility reflects the similarity between an individual’s social destination and their parents’ social origin [14], and it is typically measured by career and generational changes in socioeconomic levels of occupations [15].

The research object of this paper is corporate social mobility, but it is not the same as the mobility of production factors such as labor, capital, technology, and data. Corporate social mobility has the following characteristics: (1) corporate social mobility presupposes the mobility of land factors; (2) land factors are a natural factor; (3) since land itself cannot be mobile, land factors mobility refers to land quotas’ (construction land quotas) mobility; (4) the transfer of land development rights with the mobility of land factors allows for the spatial optimization of land development rights and increase corporate social mobility. The death of developed corporates and inflow of new corporates are not the scope of this paper.

2.1.2. Manifestations of Corporate Social Mobility

Through the reduction in inefficient construction land, construction land space can be freed up to increase the mobility of corporates and industries. By compensating old, inefficient corporates and industries, they are allowed to withdraw from construction land space and introduce new, high-efficiency corporates and industries, promoting corporate social mobility, driving industrial upgrading, and realizing industrial structure optimization. The mobility of land factors from inefficient to efficient spaces contributes to more efficient land use and promotes corporate social mobility.

For illustrative purposes, assume that A, B, and C are the number of efficient, intermediate efficient, and inefficient corporates, respectively. As a result of land factors’ mobility, A increases or A/(A+B+C) increases, and C decreases or C/(A+B+C) decreases. That is, there were many industries with a low technology level before, but now through construction land reduction, the elimination of low-quality corporates is realized and corporate social mobility is enhanced. From 2008 to 2015, the annual number of new private corporate registrations in eight suburban areas of Shanghai, showed an overall upward trend (see Figure 1).

An increase in the number of corporates reflects the increased dynamism of the economy. One of the possible reasons why private corporates have been able to add significantly is the growth in development space freed up by construction land reduction. Looking at the data on industrial corporates (see Figure 2), the number of industrial corporates in eight suburban areas showed a general downward trend from 2008 to 2015. Combining Figure 1 and Figure 2, it can be found that while the number of industrial corporates declined, the total number of corporates was on the rise, indicating that the reduction in industrial corporates provided construction land space for non-industrial corporates.

2.2. Institutional Arrangement and Corporate Social Mobility

2.2.1. Construction Land Reduction and Corporate Social Mobility

Under China’s current incentive system for political promotion, GDP is the main criterion for cadres’ performance appraisal [16], and local officials have incentives to make local GDP performance bigger in order to get promoted [17,18,19]. As a result, local governments pursue economic growth through national “hunger and thirst” investment. This phenomenon is particularly evident in eastern China [20]. Local governments increase their fiscal revenues through differentiated transfer prices for industrial, residential, and commercial land [21,22]. This fragmentation of the land market stimulates investment and raises housing prices, weakening the effects of fiscal policy [23]. Corporates are attracted to move in through low-priced land, arbitrary site selection, unapproved land use, preferential, and tax-exempt land use. This creates the formation of de facto sites and the fact that quotas for construction land are being used. The phenomenon of the scattered layout of corporates and the entry of both high-quality and low-quality corporates has led to a consequence that corporates exceed the actual scale of demand for land, and thus the entire region exceeds the scale of social demand for land, leading to inefficient land use and overuse consequences. In addition, in order to use the land in accordance with national standards and requirements, highly efficient and high-quality industrial land can be supplemented with title deeds, and also through moderate planning adjustments to bring licensed sites into compliance with planning requirements. The decline of backward industries is accelerating and construction land contradiction is becoming more and more prominent. The more developed the land-management technology and the stricter the ecological protection, the more difficult it is for backward industries to improve quality and increase efficiency. As intensity control (land-access standards) and total control become stricter, the stricter the planning, and the more difficult it is to adjust the planning. As a result, it will be difficult for poor quality corporates to make up their title deeds because they cannot meet the land-use standards, and it will be difficult to adjust the planning of strictly controlled areas. The problems are: (1) Poor quality corporates themselves improving land-use efficiency is difficult, the status quo of inefficient land use of corporates is difficult to change. This leads to problems of inefficiencies in inefficient corporates themselves. (2) It is difficult to transfer property rights to construction land, and if there is no right certificate, it is also difficult to obtain a new right certificate. The transfer price is much higher than the new offer price, which affects the motivation of those who transfer the land. It is difficult to match the size between the original site and the new site. Then, there is the inefficient transfer of inefficient construction land. (3) This leads to inefficiencies in the allocation of space quotas and macro-use conversion.

Without space for construction land, new high-quality corporates cannot be located, and these problems of inefficiencies cannot be solved. As a special institutional arrangement, construction land reduction resolves these problems by facilitating the transfer of property rights of construction land [3,4]. Through construction land reduction, more construction land quotas can be formed and stored. In this process, the original use of local interests is not damaged (the original land input, other inputs are returned). The mobility of low-utility land corporates is enhanced. After receiving compensation, low-utility land corporates invest in new areas, reducing the amount of corporate capital stranded on inefficient construction sites. The new users of land quotas have a gain (willing to fund and able to revitalize the low-utility land), and the government can spatially optimize the old and new land quotas. Thus, the problem of inefficiencies of optimal spatial allocation and macro-use conversion is solved.

Through the collection and storage of construction land quotas, the withdrawal of old inefficient corporates will be accelerated, and the social mobility of inefficient corporates will be enhanced. Construction land reduction creates a construction land “quota bank”, which provides construction land space for the realization of the plan: (1) Bringing about changes in the ratio of factors. The reduced plots conforming to construction land planning will be transferred again, the land access standard will be improved, and new high-quality corporates will enter, which will promote the mobility of land factors, and the proportion of high-quality corporates will increase. Land quotas obtained from construction land reduction provide space for the realization of the plan, and there are strict entry criteria for new corporates to enter the process. According to the Jinshan District Industrial Land Use Project Access Criteria Guide (Trial Implementation) (Jin Merchants Office [2021] No. 3), priority is given to securing land supply for high-quality investment subjects, and there are clear requirements for industrial positioning, environmental protection and safety, and scientific and technological innovation. For new land projects, in terms of performance indicators, the lower reference standard for the input and output of industrial projects is proposed: fixed asset investment intensity of no less than 5 million CNY/mu, output intensity of no less than 8 million CNY/mu/year, and tax intensity of no less than 800,000 CNY/mu/year (1 mu ≈ 0.0667 ha.). Strengthening the supervision after the event, for breach of contract in accordance with the contract agreement to be lawful and for reasonable disposal, including that the total annual tax revenue to reach production is determined to be less than 60% of the agreed standard, the land grantor has the right to terminate the contract and take back the land-use rights. The entry of new high-quality corporates further brings about an increase in the level of the industry. (2) Spatial optimization. In the process of construction land reduction, the reduced land parcels that conform to the construction land plan are re-offered, and according to the principle of efficiency first, land quotas will be mainly used in suburban areas with comparative advantages. The clustering of corporates and industries within the centralized construction area and the diffusion of technology have a scale and agglomeration effect, and they strengthen competition among corporates and industries, which helps to achieve an overall improvement in the efficiency of construction land.

The contradiction between reduction location and new location leads to spatial injustice. This spatial injustice promotes the mobility of land factors, which in turn contributes to the resolution of land contradiction. The spatial injustice is an institutional arrangement, a mechanism for the use of quotas. There is this institutional arrangement to meet economic development. Land factors flowing to higher productivity sectors can promote economic growth [24]. Specifically, (1) due to this institutional arrangement, new corporates enter the region and urban land-use clubs have new members of society, increasing mobility. (2) Expected mobility. As a result of this institutional arrangement, more corporates have the hope of entering Shanghai, raising the expectation of land-use liquidity. (3) This institutional arrangement enables inefficient construction land, which cannot be transferred, to be transferred, increasing the liquidity of the market. (4) Through the overall adjustment of uses, it increases intra-regional mobility. (5) Through inter-regional quotas flows, mobility in economically developed regions is increased. The conceptual model in this paper is shown in Figure 3.

Accordingly, Hypothesis 1 is proposed.

Hypothesis 1 (H₁).

Construction land reduction as an institutional arrangement facilitates the mobility of land factors and helps to enhance corporate social mobility.

2.2.2. Heterogeneous Impact of Land Use Planning

Urban development under private ownership of land in the West relies on property relations [25]. In China, the allocation of space for urban development is influenced by spatial planning, use control, and plan management. In Shanghai, town-level “country unit village planning” has been prepared since 2014, and later “detailed control planning” has been prepared. In the process of preparing these plans, industrial land is clearly planned, and the layout and size of construction land is planned and clearly defined. This policy was dovetailed with the Shanghai Urban Master Plan (2017–2035) in 2017. Each district has also developed plans accordingly. As such, the plans guide the allocation of land for construction, investment, etc. According to each district’s plans, districts are divided into three categories, namely, planned incremental-type areas, planned balanced-type areas, and planned decremental-type areas. Compared to the planned incremental-type areas, the planned decremental-type areas and balanced-type areas have comparative advantages in carrying out construction land reduction, with more space for construction land freed-up through construction land reduction and thus have better corporate social mobility, especially the planned decremental-type areas. Accordingly, Hypothesis 2 is proposed.

Hypothesis 2 (H₂).

Compared to planned incremental-type areas, planned decremental areas have better corporate social mobility.

3. Research Design and Data Sources

3.1. Model Setting

Detailed data on urban and rural land transfers are difficult to obtain due to the sensitivity of relevant data [26]. From the perspective of the spatial structure optimization of construction land, the evaluation of residents on the policy effects of optimizing the spatial layout of land use is used as a proxy indicator for corporate social mobility. Specifically, using the five-tier Likert scale, residents’ evaluation of the policy effect on optimizing land use and spatial layout is divided into five levels. Since the explained variables CSM_i are ordered data, ordinary least squares (OLS) estimation is no longer applicable. Drawing on the ordered probit model [27], the model is set up as follows:

CSM_i = F(β∙CLR_i + γ∙X_i + ε_i)

(1)

In Equation (1), CSM_i is explained as variable, which represents the perceived corporate social mobility by residents, and i denotes the resident i. The values of CSM_i are from 1 (strongly disagree) to 5 (strongly agree). CLR_i is our most concerned explanatory variable, which reflects the intensity of construction land reduction. X_i represents control variables. Since residents’ perceptions of policy are also influenced by household characteristics [28], X_i includes residents’ household characteristics in addition to a range of other factors that affect their perceptions of corporate social mobility and variables that reflect individual characteristics. F(·) is a nonlinear function expressed in the following form:

$$F\left({{CSM}_i}^{*}\right)=\left\{\begin{array}{c}1 {{CSM}_i}^{\mathrm{*}}<{\mu }_1\\ 2 {\mu }_1<{{CSM}_i}^{\mathrm{*}}<{\mu }_2\\ ⋮ ⋮ \\ J {{CSM}_i}^{\mathrm{*}}>{\mu }_{J-1}\end{array}\right.$$

(2)

In Equation (2), the μ₁ < μ₂ < μ₃ < … < μ_J−1 are called cut points. CSM_i* represents latent variables, and satisfies:

CSM_i^* = β·CLR_i + γ·X_i + ε_i

(3)

3.2. Variables and Indicators

This paper uses reduction intensity based on the development space of construction land to measure construction land reduction. Specifically, see

Table 1. Variable descriptions.

Variable Type	Variable Name	Variable Code	Unit	Description
Explained variables	Corporate social mobility	CSM	-	Evaluation of the effectiveness of planning and policies for construction land reduction: optimizing land use and spatial layout. Strongly agree = 3; Quite agree = 2; neutral, quite disagree, strongly disagree = 1
		CSMsd	-	Standardized CSM standardization
Core explanatory variables	Room for growth in construction land per unit of construction land	CLR	-	(planned cla in 2035—cla in 2016)/cla in 2016
	Room for growth in construction land per unit of administrative area	CLR2	-	(planned cla in 2035—cla in 2016)/administrative area
	Room for growth in construction land per unit of resident population	CLR3	-	(planned cla in 2035—cla in 2016)/resident population in 2016
	Proportion of construction land outside the development boundary to total construction land in each district	CLR4	%	cla outside the development boundary in 2016/cla in 2016
Control variables	Reduction pressure of districts	RP	%	(reduction cla outside the development boundary in 2035—new cla within the development boundary in 2035)/ cla in 2016. The intensity varies from year to year with use and decrease, thus the choice of 2035.
	Location of townships	LnTL	kilometers	Log value of distance from town to district seat
	Level of economic development of townships	LnCLGDP	million CNY/km2	Log value of above-scale GDP per unit of construction land area in 2020
	Intensity of fixed asset investment of townships	LnCLFAI	million CNY/km2	Total planned fixed asset investment per unit of construction land area in 2020
	Energy-use efficiency of townships	EE	tons of standard coal/10,000 CNY	Total energy consumption of above-scale industries per unit of gross value of above-scale industrial output in 2020
	Development pressures of townships	DP	%	Gross value of above-scale industrial output in 2020/Gross value of above-scale industrial output in 2016
	Urbanization rate of townships	UR	%	Urban population/total population
	Gender	GEN	-	1 = male; 0 = female
	Age	AGE	-	≤30 years = 1; 31–45 years = 2; 46–59 years = 3; ≥60 years = 4
	Education	EDU	-	Primary school and below = 1; Junior high school = 2; High school = 3; Junior college and above = 4
	Household income	FI	-	≤50,000 CNY = 1; 50,000–100,000 CNY = 2; 100,000–200,000 CNY= 3; ≥ 200,000 CNY = 4
	Family demographics	FPS	%	Share of population aged 18–59 in total household size
Heterogeneous variables	Planned decremental-type area	Z	-	Yes = 1, No = 0
	Planned balanced-type area	V	-	Yes = 1, No = 0
	Resident status	GB	-	Cadres of village, cadres of townships and above = 1; others = 0

Note: cla represents the area of construction land; “-” represents no unit.

. In terms of control variables selection, the push–pull theory in population movement is borrowed to study corporate social mobility. Population mobility is the result of a combination of the pull of favorable living conditions in the population inflow and the push of unfavorable living conditions in the population outflow [29,30]. Intermediate barrier factors and individual factors are also factors that affect population mobility [31]. The intermediate barrier factors mainly include institutional arrangement, distance, etc. Drawing on this idea, this paper argues that corporate social mobility is also influenced by both “push (driving force)” and “pull (resistance)”. In this paper, we control for such control variables as reduction pressure of districts, location of townships (China has a five-tier administrative system: national, provincial, municipal, county, and township. The township level is the main implementation body of construction land reduction [32]. The township plan is the basis for basic subjects to implement construction land reduction [2]), level of economic development of townships, intensity of fixed asset investment of townships, energy-use efficiency of townships, development pressure of townships, urbanization rate of townships, and individual and household characteristics of residents.

We add the resident status variable (GB) to analyze the heterogeneity impact of resident status. We also consider the heterogeneity impact of land-use planning. The study area is divided into planned incremental-type area (U), planned balanced-type area (V), and planned decremental-type area (Z) based on the established literature [2,32]. In the regression analysis, the area U is used as the reference. Table 1 shows the variable descriptions.

The distribution of explained variable is shown in Figure 4. From Figure 4, it can be observed that more than 85% of residents believe that construction land reduction contributes to corporate social mobility.

3.3. Data Sources

Some of the data were obtained from March to May 2021 through questionnaires. Y, W, and X District in Shanghai are selected [2]. A spatial episodic was applied to the questionnaires [33]. The number of questionnaires distributed, recovered, and valid were 2400, 2354 and 2192, respectively. Each street and town is the responsible and implementation body of the reduction work [4]; macro statistics are obtained from the statistical yearbooks of each district. Distance data were extracted from the administrative maps of each district using ArcGIS. The Master Plan and General Land Use Plan (2017–2035) of each district were used to delineate planning types. Basic information about the survey sample is presented in

Table 2. Statistics on basic information of respondents.

Items	Classifications	Number of Frequencies (pcs)	Percentage (%)	Cumulative Percentage (%)
GEN	Female	968	44.16	44.16
	Male	1224	55.84	100.00
AGE	≤30	318	14.51	14.51
	31–45	827	37.73	52.24
	46–59	673	30.70	82.94
	≥60	374	17.06	100.00
EDU	Primary school and below	213	9.72	9.72
	Junior high school	485	22.13	31.84
	High school	412	18.80	50.64
	Junior college and above	1082	49.36	100.00
FI	≤50,000 CNY	360	16.42	16.42
	50,000–100,000 CNY	588	26.82	43.25
	100,000–200,000 CNY	719	32.80	76.05
	≥200,000 CNY	525	23.95	100.00
Number of persons aged 18–59 in the household	0	311	14.19	14.19
	1	137	6.25	20.44
	2	856	39.05	59.49
	3	657	29.97	89.46
	4 and above	231	10.54	100.00
GB	Cadres of village and above	609	27.78	27.78
	Other	1583	72.22	100.00

. Descriptive statistics are presented in

Table 3. Descriptive statistics.

Variable	Obs	Mean	Std. Dev.	Min	Max
CSM	2192	2.3764	0.7132	1.0000	3.0000
CSMsd	2192	0.0000	1.0000	−1.9298	0.8744
CLR	2192	−0.1359	0.1518	−0.3704	0.4401
CLR2	2192	−0.0186	0.0379	−0.0687	0.0844
CLR3	2192	−0.3834	0.4768	−0.9846	2.0286
CLR4	2192	57.5099	14.3069	34.3245	66.7797
Z	2192	0.7418	0.4378	0.0000	1.0000
V	2192	0.1683	0.3743	0.0000	1.0000
GB	2192	0.2778	0.4480	0.0000	1.0000
RP	2192	8.1316	3.7304	0.0501	10.1695
LnTL	2192	2.4523	1.1146	−0.6280	3.6226
LnCLGDP	2192	8.6499	1.4969	5.6440	11.8758
LnCLFAI	2192	8.4478	1.1520	6.4379	11.8871
EE	2192	0.0876	0.0429	0.0248	0.2531
DP	2192	105.9336	34.4497	59.0643	191.6667
UR	2192	43.2322	25.3572	1.2364	100.0000
GEN	2192	0.5584	0.4967	0.0000	1.0000
AGE	2192	2.5032	0.9390	1.0000	4.0000
EDU	2192	3.0780	1.0478	1.0000	4.0000
FI	2192	2.6428	1.0186	1.0000	4.0000
FPS	2192	63.7037	33.9932	0.0000	100.0000

.

4. Empirical Results and Analysis

4.1. Baseline Regression Results and Analysis

Figure 5 shows the empirical flowchart. Before regression analysis, the variables are first tested for multicollinearity using the variance inflation factor (VIF). Generally, when the value of VIF exceeds 10, it indicates the presence of severe multicollinearity [34].

Table 4. Multicollinearity test I.

Variable	VIF	1/VIF
CLR	5.48	0.18
LnCLGDP	4.86	0.21
UR	4.17	0.24
RP	4.01	0.25
LnCLFAI	2.77	0.36
DP	2.71	0.37
EE	2.67	0.37
LnTL	2.02	0.50
EDU	1.98	0.51
AGE	1.75	0.57
FI	1.70	0.59
FPS	1.24	0.81
GEN	1.01	0.99
Mean VIF	2.80

demonstrates the results of multicollinearity test. It can be found that the VIF values of all variables are well below 10; thus, there is no serious multicollinearity.

Table 5. Benchmark regression results.

Variable	(1)	(2)	(3)	(4)
	CSM	Pr (CSM = 1)	Pr (CSM = 2)	Pr (CSM = 3)
CLR	0.8748 **	−0.1792 **	−0.1696 **	0.3487 **
	(0.4026)	(0.0825)	(0.0786)	(0.1605)
RP	0.0193	−0.0039	−0.0037	0.0077
	(0.0133)	(0.0027)	(0.0026)	(0.0053)
LnTL	0.0635 **	−0.0130 **	−0.0123 **	0.0253 **
	(0.0310)	(0.0063)	(0.0061)	(0.0123)
LnCLGDP	−0.1863 ***	0.0382 ***	0.0361 ***	−0.0743 ***
	(0.0369)	(0.0076)	(0.0074)	(0.0147)
LnCLFAI	0.0899 **	−0.0184 **	−0.0174 **	0.0359 **
	(0.0367)	(0.0075)	(0.0072)	(0.0146)
EE	2.1748 **	−0.4454 **	−0.4216 **	0.8670 **
	(0.9622)	(0.1966)	(0.1887)	(0.3836)
DP	0.0029 **	−0.0006 **	−0.0006 **	0.0012 **
	(0.0013)	(0.0003)	(0.0002)	(0.0005)
UR	−0.0055 ***	0.0011 ***	0.0011 ***	−0.0022 ***
	(0.0021)	(0.0004)	(0.0004)	(0.0008)
GEN	0.0147	−0.0030	−0.0028	0.0058
	(0.0504)	(0.0103)	(0.0098)	(0.0201)
AGE	0.0685 *	−0.0140 *	−0.0133 *	0.0273 *
	(0.0353)	(0.0072)	(0.0069)	(0.0141)
EDU	0.1779 ***	−0.0364 ***	−0.0345 ***	0.0709 ***
	(0.0332)	(0.0068)	(0.0067)	(0.0132)
FI	0.1059 ***	−0.0217 ***	−0.0205 ***	0.0422 ***
	(0.0317)	(0.0065)	(0.0062)	(0.0126)
FPS	−0.0001	0.0000	0.0000	−0.0000
	(0.0008)	(0.0002)	(0.0002)	(0.0003)
/cut1	−0.5477
	(0.3849)
/cut2	0.5687
	(0.3853)
Wald test	168.62 ***
Pseudo R2	0.0404
Observations	2192	2192	2192	2192

Note: Column (1) is Robust Std. Err. in parentheses; Columns (2)–(4) are marginal effects, Delta-method Std. Err. in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1.

shows the benchmark regression results. Column (1) is the benchmark regression result. It controls for the control variables of reduction pressure of districts, location of townships, level of economic development of townships, intensity of fixed asset investment of townships, energy-use efficiency of townships, development pressure of townships, urbanization rate of townships, and the individual and household characteristics of residents.

Column (1) reveals that construction land reduction significantly promotes corporate social mobility. The research hypothesis H₁ is tested. Through construction land reduction, inefficient corporates have been reduced, the standard of newly introduced corporates has increased, and high-quality corporates have increased. From 2014 to 2020, the eight suburban districts all achieved positive growth in gross industrial output value, with the real gross industrial output value increasing by 25.75% on average (calculated based on the Shanghai Statistical Yearbook). Through construction land reduction, construction land space is provided for the planning implementation. Through the upgrading of land access standards, including the average tax per mu, average output per mu, and pollution intensity, all of which are managed at a high standard, only competitive corporates can enter. The entry of new corporates strengthens competition and also makes the original scattered construction land into industrial zones and causes the centralized construction area to concentrate, forming a scale effect [3]. As a result of the concentration of corporates, spillover benefits among infrastructure, including sewage systems and roads, are accentuated, bringing about agglomeration benefits. In addition, the mutual use of products as inputs and outputs among corporates helps to reduce transportation costs and strengthens collaboration and division of labor among corporates, thus driving the development of industrial clusters.

For the control variables, the effect of the reduction pressure of districts on corporate social mobility is not significant.

Location can affect construction land reduction [3]. Construction land reduction is targeted at inefficient and poorly located construction land. The worse the location of the township, the more the district positions the township as a priority township for reduction, the more space can be freed up for construction land, and therefore the more it contributes to the enhancement of corporate social mobility.

Corporate social mobility is influenced by economic development. The more the economy develops, the greater the demand for construction land quotas, and the greater the need to provide space for the landing of new industries and projects. It is relatively difficult to carry out construction land reduction in regions with a better economic base, which makes it difficult to free up space for construction land, and therefore corporate social mobility is relatively poor.

The intensity of fixed asset investment of townships has a catalytic effect on corporate social mobility. Investment is necessary for the introduction of corporates and the expansion of reproduction, and an increase in the intensity of investment contributes to corporate social mobility.

The lower the energy-use efficiency of a region, the stronger the incentive to reduce construction land, and the greater the need to eliminate outdated capacity, leading to increased corporate social mobility. The lower the energy-use efficiency of a region, the more backward its industries are. Excessive growth in energy consumption will force corporates to transform, promote regional industrial upgrading, and eliminate backward capacity. Thus, industrial backwardness can accelerate corporate social mobility.

Development pressure significantly contributes to corporate social mobility. Government departments set targets for GDP in short-, medium-, and long-term planning. GDP is the focus of performance appraisal in government departments, and the greater the pressure for economic development, the greater the incentive for local officials to make local GDP performance greater for promotion. The greater the pressure on economic development, the more urgent the optimization of industrial structure. The greater the pressure of performance appraisal and economic development, the greater the need to free up space for construction through the flow of land factors. As a result, the stronger the corporate social mobility.

Urbanization can increase the carrying capacity of land and also lead to the expansion of construction land [35]. In order to control urban sprawl, China exercises strict control over construction land. The higher the urbanization rate, the higher the demand for the efficient use of land resources. There is an urgent need to promote the transfer of land factors, revitalize inefficient and idle land resources, and improve the efficiency of land use. As construction land reduction enters the third phase, the pressure for reduction is increasing. On the one hand, because inefficient construction land has been reduced in the earlier stages, the construction land efficiency is higher than before; on the other hand, the reduction in state-owned corporates is beginning to be explored, and there is great resistance to this reduction in order to avoid the loss of state-owned assets. As construction land carries more employment at this stage, the continued implementation of construction land reduction will have a certain negative impact on employment in the reduction areas. The higher the urbanization rate, the wider the scope of the population and interest compensation involved, and the higher the reduction cost, which to some extent hinders corporate social mobility.

At the micro level, increases in residents’ age, education, and household income all contribute to enhancing residents’ perceptions of corporate social mobility. According to human capital theory, education is an important investment in human capital and is an important cause of population mobility. An increase in the level of education of the population contributes to its competitiveness on the job market. The better the talent base needed for business renewal and industrial upgrading, the more likely it is to accelerate corporate social mobility. The higher level of education of residents and better knowledge of construction land reduction contribute to strengthening their perception of corporate social mobility. The higher the level of household income of residents, a group with a comparative advantage, the better their policy perception and support for construction land reduction, and the stronger their perceived corporate social mobility.

4.2. Analysis of Marginal Effects

We perform further calculations to find the marginal effects of each explanatory variable on corporate social mobility. We calculate how corporate social mobility is affected by a unit change in the explanatory variable taking each value when all explanatory variables are at their mean values. As in Equation (4),

$${\left.\frac{\partial Prob(CSM=i|x)}{\partial x}\right|}_{x=\overline{x}}(i=\mathrm{1,2},3)$$

(4)

In Equation (4), x denotes explanatory variables. According to Equation (4), we can explain how a one-unit change in x affects the probability of corporate social mobility taking each value. We use corporate social mobility and construction land reduction as explained and explanatory variables, respectively, and control for the control variables. Regressions are estimated using ordered probit models. Columns (2)–(4) of Table 5 show the predicted probabilities of largely disagree, quite agree, and strongly agree, respectively. When all explanatory variables take the mean, each unit increase in construction land reduction strength will lead to a 34.87 percentage-point increase in the probability of strongly agree and a significant 16.96 percentage-point and 17.92 percentage-point decrease in the probability of quite agree and largely disagree, respectively. Thus, construction land reduction helps to enhance corporate social mobility.

4.3. Robustness Tests

4.3.1. Transforming the Core Explanatory Variables

We use corporate social mobility and CLR₂ as explained and core explanatory variables, respectively, and control variables are controlled. Regression estimation is performed using the ordered probit model.

Table 6. Multicollinearity test II.

Variable	VIF	1/VIF
LnCLGDP	4.31	0.23
RP	3.49	0.29
UR	3.30	0.30
CLR2	3.24	0.31
DP	2.36	0.42
LnCLFAI	2.33	0.43
LnTL	2.13	0.47
EE	2.01	0.50
EDU	1.98	0.51
AGE	1.75	0.57
FI	1.69	0.59
FPS	1.24	0.81
GEN	1.01	0.99
Mean VIF	2.37

demonstrates the results of the multicollinearity test. It can be found that the VIF values of each variable are well below 10, and thus there is no serious multicollinearity.

Table 7. Robustness test I.

Variable	(1)	(2)	(3)	(4)
	CSM	Pr (CSM = 1)	Pr (CSM = 2)	Pr (CSM = 3)
CLR2	3.0570 **	−0.6257 **	−0.5930 **	1.2187 **
	(1.2155)	(0.2497)	(0.2375)	(0.4846)
RP	0.0130	−0.0027	−0.0025	0.0052
	(0.0124)	(0.0025)	(0.0024)	(0.0049)
LnTL	0.0737 **	−0.0151 **	−0.0143 **	0.0294 **
	(0.0320)	(0.0065)	(0.0063)	(0.0128)
LnCLGDP	−0.2014 ***	0.0412 ***	0.0391 ***	−0.0803 ***
	(0.0349)	(0.0072)	(0.0071)	(0.0139)
LnCLFAI	0.1054 ***	−0.0216 ***	−0.0204 ***	0.0420 ***
	(0.0336)	(0.0069)	(0.0066)	(0.0134)
EE	2.8590 ***	−0.5852 ***	−0.5546 ***	1.1397 ***
	(0.8188)	(0.1669)	(0.1629)	(0.3264)
DP	0.0034 ***	−0.0007 ***	−0.0007 ***	0.0013 ***
	(0.0012)	(0.0002)	(0.0002)	(0.0005)
UR	−0.0050 ***	0.0010 ***	0.0010 ***	−0.0020 ***
	(0.0018)	(0.0004)	(0.0004)	(0.0007)
GEN	0.0134	−0.0027	−0.0026	0.0053
	(0.0504)	(0.0103)	(0.0098)	(0.0201)
AGE	0.0689 *	−0.0141 *	−0.0134 *	0.0275 *
	(0.0353)	(0.0072)	(0.0069)	(0.0141)
EDU	0.1815 ***	−0.0372 ***	−0.0352 ***	0.0724 ***
	(0.0332)	(0.0068)	(0.0067)	(0.0132)
FI	0.1036 ***	−0.0212 ***	−0.0201 ***	0.0413 ***
	(0.0316)	(0.0065)	(0.0062)	(0.0126)
FPS	−0.0000	0.0000	0.0000	−0.0000
	(0.0008)	(0.0002)	(0.0002)	(0.0003)
/cut1	−0.3727
	(0.3505)
/cut2	0.7444 **
	(0.3511)
Wald test	167.48 ***
Pseudo R2	0.0409
Observations	2192	2192	2192	2192

Note: Column (1) is Robust Std. Err. in parentheses; Columns (2)–(4) are marginal effects, Delta-method Std. Err. in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1.

shows the regression results. Thus, the findings are robust.

We use corporate social mobility and CLR₃ as explained and core explanatory variables, respectively, and control variables are controlled. Regression estimation is performed using an ordered probit model.

Table 8. Multicollinearity test III.

Variable	VIF	1/VIF
LnCLGDP	5.34	0.19
CLR3	4.77	0.21
UR	4.40	0.23
RP	4.20	0.24
DP	2.94	0.34
LnCLFAI	2.46	0.41
EE	2.36	0.42
EDU	1.98	0.51
LnTL	1.94	0.52
AGE	1.74	0.57
FI	1.70	0.59
FPS	1.24	0.81
GEN	1.01	0.99
Mean VIF	2.78

demonstrates the results of the multicollinearity test. The VIF values of each variable are well below 10, and thus there is no serious multicollinearity.

Table 9. Robustness test II.

Variable	(1)	(2)	(3)	(4)
	CSM	Pr (CSM = 1)	Pr (CSM = 2)	Pr (CSM = 3)
CLR3	0.2057 *	−0.0422 *	−0.0398 *	0.0820 *
	(0.1201)	(0.0247)	(0.0233)	(0.0479)
RP	0.0184	−0.0038	−0.0036	0.0073
	(0.0136)	(0.0028)	(0.0026)	(0.0054)
LnTL	0.0513 *	−0.0105 *	−0.0099 *	0.0204 *
	(0.0299)	(0.0061)	(0.0058)	(0.0119)
LnCLGDP	−0.1845 ***	0.0378 ***	0.0357 ***	−0.0736 ***
	(0.0387)	(0.0080)	(0.0078)	(0.0154)
LnCLFAI	0.1063 ***	−0.0218 ***	−0.0206 ***	0.0424 ***
	(0.0346)	(0.0071)	(0.0068)	(0.0138)
EE	2.6343 ***	−0.5399 ***	−0.5102 ***	1.0502 ***
	(0.8975)	(0.1831)	(0.1773)	(0.3578)
DP	0.0030 **	−0.0006 **	−0.0006 **	0.0012 **
	(0.0013)	(0.0003)	(0.0003)	(0.0005)
UR	−0.0050 **	0.0010 **	0.0010 **	−0.0020 **
	(0.0021)	(0.0004)	(0.0004)	(0.0008)
GEN	0.0147	−0.0030	−0.0029	0.0059
	(0.0504)	(0.0103)	(0.0098)	(0.0201)
AGE	0.0652 *	−0.0134 *	−0.0126 *	0.0260 *
	(0.0352)	(0.0072)	(0.0069)	(0.0140)
EDU	0.1755 ***	−0.0360 ***	−0.0340 ***	0.0700 ***
	(0.0332)	(0.0068)	(0.0067)	(0.0132)
FI	0.1050 ***	−0.0215 ***	−0.0203 ***	0.0419 ***
	(0.0316)	(0.0065)	(0.0062)	(0.0126)
FPS	−0.0001	0.0000	0.0000	−0.0000
	(0.0008)	(0.0002)	(0.0002)	(0.0003)
/cut1	−0.3418
	(0.3537)
/cut2	0.7741 **
	(0.3546)
Wald test	167.54 ***
Pseudo R2	0.0400
Observations	2192	2192	2192	2192

Note: Column (1) is Robust Std. Err. in parentheses; Columns (2)–(4) are marginal effects, Delta-method Std. Err. in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1.

shows the regression results. Thus, the findings are robust.

We use corporate social mobility and CLR₄ as explained and core explanatory variables, respectively, and control variables are controlled. Regression estimation is performed using an ordered probit model.

Table 10. Multicollinearity test IV.

Variable	VIF	1/VIF
CLR4	7.96	0.13
LnCLGDP	6.14	0.16
RP	6.12	0.16
EE	2.82	0.35
DP	2.49	0.40
UR	2.46	0.41
LnCLFAI	2.43	0.41
EDU	1.99	0.50
LnTL	1.96	0.51
AGE	1.74	0.57
FI	1.68	0.60
FPS	1.24	0.81
GEN	1.01	0.99
Mean VIF	3.08

demonstrates the results of the multicollinearity test. The VIF values of each variable are well below 10, and thus there is no serious multicollinearity.

Table 11. Robustness test III.

Variable	(1)	(2)	(3)	(4)
	CSM	Pr (CSM = 1)	Pr (CSM = 2)	Pr (CSM = 3)
CLR4	0.0092 *	−0.0019 *	−0.0018 *	0.0036 *
	(0.0048)	(0.0010)	(0.0009)	(0.0019)
RP	−0.0117	0.0024	0.0023	−0.0047
	(0.0160)	(0.0033)	(0.0031)	(0.0064)
LnTL	0.0378	−0.0078	−0.0073	0.0151
	(0.0301)	(0.0062)	(0.0059)	(0.0120)
LnCLGDP	−0.1681 ***	0.0345 ***	0.0326 ***	−0.0670 ***
	(0.0423)	(0.0087)	(0.0083)	(0.0169)
LnCLFAI	0.1478 ***	−0.0303 ***	−0.0286 ***	0.0589 ***
	(0.0343)	(0.0070)	(0.0068)	(0.0137)
EE	2.2145 **	−0.4539 **	−0.4289 **	0.8828 **
	(0.9739)	(0.1995)	(0.1905)	(0.3882)
DP	0.0034 ***	−0.0007 ***	−0.0007 ***	0.0014 ***
	(0.0012)	(0.0002)	(0.0002)	(0.0005)
UR	−0.0028 *	0.0006 *	0.0006 *	−0.0011 *
	(0.0015)	(0.0003)	(0.0003)	(0.0006)
GEN	0.0197	−0.0040	−0.0038	0.0078
	(0.0503)	(0.0103)	(0.0097)	(0.0200)
AGE	0.0605 *	−0.0124 *	−0.0117 *	0.0241 *
	(0.0353)	(0.0072)	(0.0069)	(0.0141)
EDU	0.1858 ***	−0.0381 ***	−0.0360 ***	0.0740 ***
	(0.0333)	(0.0068)	(0.0068)	(0.0133)
FI	0.1003 ***	−0.0206 ***	−0.0194 ***	0.0400 ***
	(0.0314)	(0.0065)	(0.0061)	(0.0125)
FPS	−0.0001	0.0000	0.0000	−0.0000
	(0.0008)	(0.0002)	(0.0002)	(0.0003)
/cut1	0.5870
	(0.5217)
/cut2	1.7027 ***
	(0.5232)
Wald test	169.19 ***
Pseudo R2	0.0401
Observations	2192	2192	2192	2192

Note: Column (1) is Robust Std. Err. in parentheses; Columns (2)–(4) are marginal effects, Delta-method Std. Err. in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1.

shows the regression results. Thus, the findings are robust.

4.3.2. Transforming Estimation Methods

We use corporate social mobility with standardized values and CLR as explained and core explanatory variables, respectively, and control variables are controlled. Regressions are estimated using OLS methods. Column (1) of

Table 12. Robustness test IV.

Variable	(1)	(2)	(3)	(4)	(5)
	CSMsd	CSM	Pr (CSM = 1)	Pr (CSM = 2)	Pr (CSM = 3)
CLR	0.6532 **	1.4581 **	−0.1582 **	−0.2060 **	0.3642 **
	(0.3156)	(0.6945)	(0.0754)	(0.0987)	(0.1735)
RP	0.0192 *	0.0312	−0.0034	−0.0044	0.0078
	(0.0115)	(0.0225)	(0.0024)	(0.0032)	(0.0056)
LnTL	0.0432 *	0.1157 **	−0.0126 **	−0.0163 **	0.0289 **
	(0.0254)	(0.0519)	(0.0056)	(0.0074)	(0.0130)
LnCLGDP	−0.1453 ***	−0.3150 ***	0.0342 ***	0.0445 ***	−0.0787 ***
	(0.0308)	(0.0623)	(0.0069)	(0.0091)	(0.0156)
LnCLFAI	0.0708 **	0.1559 **	−0.0169 **	−0.0220 **	0.0389 **
	(0.0313)	(0.0628)	(0.0068)	(0.0089)	(0.0157)
EE	1.7885 **	4.0007 **	−0.4341 **	−0.5652 **	0.9993 **
	(0.8196)	(1.6400)	(0.1772)	(0.2346)	(0.4096)
DP	0.0027 **	0.0049 **	−0.0005 **	−0.0007 **	0.0012 **
	(0.0011)	(0.0021)	(0.0002)	(0.0003)	(0.0005)
UR	−0.0040 **	−0.0097 ***	0.0011 ***	0.0014 ***	−0.0024 ***
	(0.0016)	(0.0035)	(0.0004)	(0.0005)	(0.0009)
GEN	0.0076	0.0335	−0.0036	−0.0047	0.0084
	(0.0416)	(0.0842)	(0.0091)	(0.0119)	(0.0210)
AGE	0.0589 **	0.1242 **	−0.0135 **	−0.0176 **	0.0310 **
	(0.0291)	(0.0592)	(0.0064)	(0.0084)	(0.0148)
EDU	0.1473 ***	0.3038 ***	−0.0330 ***	−0.0429 ***	0.0759 ***
	(0.0282)	(0.0560)	(0.0061)	(0.0082)	(0.0140)
FI	0.0846 ***	0.1692 ***	−0.0184 ***	−0.0239 ***	0.0423 ***
	(0.0258)	(0.0530)	(0.0058)	(0.0075)	(0.0132)
FPS	−0.0000	−0.0002	0.0000	0.0000	−0.0000
	(0.0007)	(0.0014)	(0.0001)	(0.0002)	(0.0003)
/cut1		−0.8756
		(0.6456)
/cut2		1.0207
		(0.6469)
F-test	14.38 ***
Wald test		165.43 ***
Pseudo R2		0.0414
Constant	−0.6122 **
	(0.3050)
Observations	2192	2192	2192	2192	2192
R-squared	0.0744

Note: Columns (1)–(2) are Robust Std. Err. in parentheses; Columns (3)–(5) are marginal effects, Delta-method Std. Err. in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1.

shows the results. It can be seen that construction land reduction significantly enhances corporate social mobility.

We use corporate social mobility and CLR as explained and core explanatory variables, respectively, and control variables are controlled. Regressions are estimated using ordered logistic methods. Columns (2)–(5) of Table 12 shows the results. It can be seen that construction land reduction significantly enhances corporate social mobility. The findings are robust.

4.4. Heterogeneity Tests

4.4.1. Heterogeneity of Resident Status

In the local government appraisal process, higher-level governments assess both the completion of construction land reduction tasks by lower-level governments and the completion of economic development goals. This paper further identifies the heterogeneous impact of resident status. We use corporate social mobility and construction land reduction as explained and core explanatory variables, respectively, and control variables are controlled. We also add resident status as a core explanatory variable. Regressions are estimated using ordered probit models.

Table 13. Heterogeneous results of resident status.

Variable	(1)	(2)	(3)	(4)
	CSM	Pr (CSM = 1)	Pr (CSM = 2)	Pr (CSM = 3)
CLR	0.9427 **	−0.1914 **	−0.1844 **	0.3758 **
	(0.4048)	(0.0823)	(0.0798)	(0.1613)
GB	0.2932 ***	−0.0595 ***	−0.0573 ***	0.1169 ***
	(0.0683)	(0.0139)	(0.0138)	(0.0272)
RP	0.0111	−0.0023	−0.0022	0.0044
	(0.0134)	(0.0027)	(0.0026)	(0.0053)
LnTL	0.0700 **	−0.0142 **	−0.0137 **	0.0279 **
	(0.0310)	(0.0063)	(0.0061)	(0.0124)
LnCLGDP	−0.1851 ***	0.0376 ***	0.0362 ***	−0.0738 ***
	(0.0370)	(0.0076)	(0.0075)	(0.0147)
LnCLFAI	0.0917 **	−0.0186 **	−0.0179 **	0.0365 **
	(0.0366)	(0.0074)	(0.0072)	(0.0146)
EE	1.6249 *	−0.3299 *	−0.3178 *	0.6477 *
	(0.9766)	(0.1980)	(0.1922)	(0.3893)
DP	0.0027 **	−0.0006 **	−0.0005 **	0.0011 **
	(0.0013)	(0.0003)	(0.0002)	(0.0005)
UR	−0.0056 ***	0.0011 ***	0.0011 ***	−0.0022 ***
	(0.0021)	(0.0004)	(0.0004)	(0.0008)
GEN	0.0016	−0.0003	−0.0003	0.0007
	(0.0504)	(0.0102)	(0.0099)	(0.0201)
AGE	0.0634 *	−0.0129 *	−0.0124 *	0.0253 *
	(0.0353)	(0.0072)	(0.0069)	(0.0141)
EDU	0.1338 ***	−0.0272 ***	−0.0262 ***	0.0533 ***
	(0.0343)	(0.0070)	(0.0069)	(0.0137)
FI	0.0818 **	−0.0166 **	−0.0160 **	0.0326 **
	(0.0319)	(0.0065)	(0.0063)	(0.0127)
FPS	−0.0001	0.0000	0.0000	−0.0000
	(0.0008)	(0.0002)	(0.0002)	(0.0003)
/cut1	−0.8001 **
	(0.3906)
/cut2	0.3223
	(0.3909)
Wald test	180.81 ***
Pseudo R2	0.0449
Observations	2192	2192	2192	2192

Note: Column (1) is Robust Std. Err. in parentheses; Columns (2)–(4) are marginal effects, Delta-method Std. Err. in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1.

shows the regression results. Column (1) of Table 13 shows the basic regression results; Columns (2)–(4) show the predicted probabilities of largely disagree, quite agree, and strongly agree, respectively. It can be seen that construction land reduction significantly enhances corporate social mobility, which is perceived to be stronger by cadres.

4.4.2. Heterogeneous of Land-Use Planning

This paper further tests the heterogeneity impact of land-use planning. We use corporate social mobility and construction land reduction as explained and explanatory variables, respectively, and control variables are controlled. We also add land-use planning as a core explanatory variable. Regressions are estimated using ordered probit models.

Table 14. Heterogeneity results of land-use planning.

Variable	(1)	(2)	(3)	(4)
	CSM	Pr (CSM = 1)	Pr (CSM = 2)	Pr (CSM = 3)
CLR	1.8014 ***	−0.3683 ***	−0.3498 ***	0.7181 ***
	(0.6567)	(0.1346)	(0.1289)	(0.2618)
Z	0.4452 **	−0.0910 **	−0.0865 **	0.1775 **
	(0.2165)	(0.0443)	(0.0423)	(0.0863)
V	0.1663	−0.0340	−0.0323	0.0663
	(0.1086)	(0.0221)	(0.0212)	(0.0433)
RP	0.0231 *	−0.0047 *	−0.0045 *	0.0092 *
	(0.0136)	(0.0028)	(0.0027)	(0.0054)
LnTL	0.0269	−0.0055	−0.0052	0.0107
	(0.0388)	(0.0079)	(0.0076)	(0.0155)
LnCLGDP	−0.1527 ***	0.0312 ***	0.0297 ***	−0.0609 ***
	(0.0419)	(0.0086)	(0.0083)	(0.0167)
LnCLFAI	0.0643	−0.0132	−0.0125	0.0256
	(0.0396)	(0.0081)	(0.0077)	(0.0158)
EE	1.4905	−0.3047	−0.2895	0.5942
	(1.0742)	(0.2191)	(0.2098)	(0.4282)
DP	0.0028 **	−0.0006 **	−0.0005 **	0.0011 **
	(0.0013)	(0.0003)	(0.0003)	(0.0005)
UR	−0.0060 ***	0.0012 ***	0.0012 ***	−0.0024 ***
	(0.0021)	(0.0004)	(0.0004)	(0.0009)
GEN	0.0149	−0.0031	−0.0029	0.0060
	(0.0504)	(0.0103)	(0.0098)	(0.0201)
AGE	0.0711 **	−0.0145 **	−0.0138 **	0.0284 **
	(0.0353)	(0.0072)	(0.0069)	(0.0141)
EDU	0.1708 ***	−0.0349 ***	−0.0332 ***	0.0681 ***
	(0.0336)	(0.0069)	(0.0068)	(0.0134)
FI	0.1091 ***	−0.0223 ***	−0.0212 ***	0.0435 ***
	(0.0317)	(0.0066)	(0.0062)	(0.0127)
FPS	−0.0001	0.0000	0.0000	−0.0000
	(0.0008)	(0.0002)	(0.0002)	(0.0003)
/cut1	−0.4035
	(0.4204)
/cut2	0.7143 *
	(0.4213)
Wald test	175.82 ***
Pseudo R2	0.0413
Observations	2192	2192	2192	2192

Note: Column (1) is Robust Std. Err. in parentheses; Columns (2)–(4) are marginal effects, Delta-method Std. Err. in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1.

shows the regression results. Column (1) of Table 14 is the basic regression; Columns (2)–(4) show the predictions of the probabilities of largely disagree, quite agree, and strongly agree, respectively. It can be found that construction land reduction significantly enhances corporate social mobility.

Compared to area U, corporate social mobility in areas V and Z is stronger, and corporate social mobility in area Z is significantly stronger than that in area U. The greater corporate social mobility in area Z is due to the greater reduction in inefficient corporates. Compared to area U, corporates in area Z as a whole are less efficient, and there is a reduction in the number of low-quality corporates due to the “subtraction” of construction land. In terms of new corporates, fewer new corporates have been added due to strict industrial land access standards. As a result, compared to area U, area Z can free up more construction land, which is more helpful to promote corporate social mobility. Thus, the research hypothesis H₂ of this paper is tested.

5. Discussion

While land is a fundamental production factor for economic development, the existing literature has mainly focused on labor, capital, and technology factors. Economic growth is an important economic objective, which presupposes certain factor inputs [5]. Physical capital accumulation is an important cause of economic growth [6], and technological advances are also important in the long run. According to the endogenous economic growth theory, endogenous technological progress is crucial for economic growth [7,8]. Part of the literature has focused on the contribution of rural to urban land conversion to economic growth [20]. Some studies have also focused on the important role of industrial structure in macroeconomic development [24]. It has also been argued that the contribution of energy inputs to economic growth far exceeds that of capital inputs, with a 1% increase in energy consumption accelerating GDP growth in South Asia by about 3% [5].

In terms of factor mobility: (1) More literature has focused on the flow of agricultural land. Land transfer has become an important part of urban development [13]. Some studies have analyzed the restriction of legal norms of collective construction land and its invisible circulation [36]. Several studies have found that land titling has facilitated the transfer of inner suburbs’ rural housing land [28]. There are also studies that argue that orderly land transfer can promote the sustainable development of agriculture [37]. (2) Some studies have also focused on labor mobility. Declining transaction costs for land leases would substantially increase productivity as labor concentrates in the urban sector [38]. Population mobility is influenced by a game of push and pull factors [29,30,39].

In terms of construction land reduction, it meets the land demand of economic development through the optimization of construction land structure [3]. It is also a policy innovation to solve construction land contradiction. The literature has also examined the life cycle management of industrial land [40], the reduction location choices [3], and the impact of industrial land on towns and villages [4]. There are also studies that focus on the development of areas with net reductions in construction land [2].

In summary, established studies have focused on the impact of factor inputs such as labor, capital and technology on economic development, and in terms of factor mobility, they have mainly focused on the flow of agricultural land and labor, while less research has been conducted on corporate social mobility. The inefficient allocation of construction land exacerbates the scarcity of construction land. It is vital to explore new modes of high-quality utilization of land, and construction land reduction is one said way. Construction land reduction may also be applicable to countries and regions that require limited land use, such as Japan and the European Union. This paper investigates how construction land reduction as an institutional arrangement in Shanghai affects corporate social mobility. This paper also analyzes the heterogeneous of residential status and land-use planning. Restricted by the availability of data, access to relevant data on land use and corporates would lead to richer research conclusions, which is the direction of subsequent research.

6. Conclusions and Policy Implications

6.1. Conclusions

This paper investigates how construction land reduction as an institutional arrangement in Shanghai affects corporate social mobility. The following findings are discussed: (1) Construction land reduction is an important institutional arrangement for solving problems of inefficiencies such as inefficient corporates, the transfer of inefficient construction land, spatial quotas’ allocation, and macro use conversion. (2) Construction land reduction significantly promotes corporate social mobility. (3) Compared to non-cadre residents, cadres perceive greater corporate social mobility. (4) Compared to planned incremental-type areas, planned decremental-type areas are the key areas for construction land reduction, which can free up more space for construction land and are more conducive to improving corporate social mobility.

6.2. Policy Implications

Shanghai has reached a “ceiling” for construction land, which reduces land for new entrants and creates a negative expectation of corporate social mobility. Increasing liquidity expectations will facilitate the entry of superior corporates. (1) Construction land reduction is of great practical importance for optimizing land’s allocation and promoting development at this stage and should be continued. (2) Construction land reduction enhances corporate social mobility, which is important for enhancing the dynamism of regional economic development. (3) Since planned decremental-type areas provide more space for construction land, it is important to focus on the development of planned decremental-type areas while promoting corporate social mobility. Planned decremental-type areas are the focus of construction land reduction, and its construction land allocation is in a disadvantageous position [3]. This is detrimental to its long-term development and should be emphasized in the process of land factors’ flow. Through technical support from developed regions, it helps to improve energy-utilization efficiency and investment intensity, and promotes land factors’ flow. (4) It is necessary to enhance the reduction in areas with poor location conditions and poor energy-use efficiency, so as to better realize the spatial optimization of construction land and enhance corporate social mobility.