How Does Land Misallocation Weaken Economic Resilience? Evidence from China

Abstract

Drawing on evidence from China’s land market, this study systematically investigates the impact of land misallocation on economic resilience and reveals the underlying mechanism that operates by suppressing technological advancement. A theoretical model of economic resilience is developed, incorporating technology and factor allocation. Empirical analysis is conducted using a panel dataset of 95 Chinese cities (2012–2024) through spatial econometric and mediation models. The findings indicate that land misallocation significantly reduces local economic resilience and exhibits negative spatial spillover effects. The core mechanism is identified as follows: subsidies via low-priced industrial land delay the market exit of low-efficiency firms, hindering the reallocation of production factors to more productive sectors. This suppression of technological progress ultimately weakens a region’s capacity to withstand external shocks. Based on the findings, policy implications include optimizing land supply structure, accelerating fiscal system reform, and strengthening policy coordination.

1. Introduction

At the present stage, the world economic situation is experiencing significant fluctuations. Under the influence of trade friction and geopolitical games, how to enhance regional economic resilience has increasingly become a focal point of current research. Economic resilience is not only an important manifestation of high-quality economic development but also reflects the resistance and recovery capacity of an economic system when facing exogenous shocks. The existing research has indicated that the mobility efficiency and optimal allocation of production factors are central to economic resilience [1]. However, most studies have primarily focused on the allocation of mobile factors such as labor and capital, while overlooking the critical role of land as a key factor [2]. Particularly in developing countries like China, the land factor can often be subject to supply regulation by the government. Therefore, in practice, as an important lever for local governments to regulate the economy, the allocation efficiency of land may have a more profound impact on regional economic resilience. China’s factor marketization reform has long prioritized labor and capital factors, while the marketization process for the land factor has been relatively lagging. It was not until 2020 that the Chinese government formally included the land factor within the scope of market-oriented reforms for the first time and proposed measures such as allowing cross-regional trading of land supply quotas. However, the optimization of land factor allocation still faces numerous challenges. Consequently, this study aims to build upon the theory of factor misallocation and use the Chinese land market as a sample to analyze how land factor misallocation affects regional economic resilience.

Early theoretical research focusing on the impact of factor misallocation on regional economies, such as the classic model by Hsieh & Klenow (2009), primarily concentrated on the analysis of how labor and capital factors affect Total Factor Productivity (TFP) [3], paying little attention to the economic effects arising from the distorted allocation of the land factor. While subsequent scholars have also employed land misallocation as a primary explanation for agricultural productivity disparities between rich and poor countries, and Adamopoulos and Restuccia (2014) were the first to analyze the relationship between land misallocation and agricultural productivity using micro-level samples based on the model constructed by Hsieh & Klenow (2009) [3,4,5], most of these studies focus on the connection between land misallocation and agricultural productivity, seldom extending their inquiry to other industrial sectors or urban economies. However, within the context of China’s unique land system—where local governments can determine the supply quantity and price of land for different uses—the land factor, conversely, becomes a crucial lever for regional economic regulation. Local governments can dictate the allocation of land factors across different uses and industries, and the particularity of local governments’ objective functions may lead to structural distortions in land factor allocation [6]. The root of this distortion can be traced back to China’s tax-sharing reform—due to the mismatch between “fiscal authority” and “administrative responsibility,” land conveyance revenue has become a core source of local government fiscal revenue. In 2022, the total revenue from land transfers and related taxes in China exceeded 10 trillion-yuan, accounting for 9.1% of GDP. In some cities, fiscal revenue reliance on the land factor even exceeded 90%. To attract investment, Chinese local governments commonly adopt a “Price Scissors Gap” strategy, involving the low-price supply of industrial land and the high-price supply of commercial land, forming an implicit subsidy for industrial land [7]. However, this subsidy mechanism may allow inefficient enterprises to delay market exit due to cost advantages, thereby creating a “crowding-out effect” on efficient enterprises and inhibiting industrial upgrading and innovation [8]. In the long run, this land misallocation not only reduces the efficiency of resource allocation but may also weaken the regional economy’s ability to cope with external shocks, thus negatively impacting economic resilience. Therefore, this research focuses on the underlying mechanism of this price misallocation of the land factor across different supply uses on regional economic resilience.

In recent years, research by Chinese scholars has gradually begun to pay attention to the economic effects of land misallocation [9]. However, these studies mostly concentrate on the impact of land misallocation on economic growth or industrial structure, seldom touching upon economic resilience. Some scholars point out that land misallocation may indirectly affect economic resilience by suppressing enterprise TFP, crowding out R&D investment, or exacerbating local government debt risks [10], but this mechanism has not yet been systematically tested. Furthermore, the existing research is divided on the industrial agglomeration effects of land misallocation: on one hand, the lower price of industrial land within land price misallocation can promote the expansion of industrial scale, benefiting economic growth; on the other hand, this land price misallocation may also lead to inefficient enterprises remaining in the market, forming “inefficient agglomeration,” and ultimately impairing long-term economic resilience [11]. Regional economic resilience draws from the concepts in physics and ecology of a system’s capacity to change, adapt, and transform in the face of external pressure and disturbance, leading to its definition in regional economics as the developmental capacity of a region to resist risk shocks and navigate uncertainty [12]. Following the 2008 “Subprime Mortgage Crisis”, research on economic resilience gradually became a hotspot in academia. The early literature emphasized the role of industrial structure diversification in promoting economic resilience, while recent studies have focused more on the impact of innovation capacity and production efficiency on economic resilience [13]. However, most of these studies have overlooked the critical role of local governments in land resource allocation. In China, the degree of administrative intervention in the land factor is far higher than that for labor and capital. Its misallocation may systematically impact regional economic resilience through channels such as affecting industrial upgrading, enterprise innovation, and fiscal stability. Regrettably, there is currently a lack of empirical research directly exploring the relationship between land misallocation and economic resilience, let alone addressing the spatial correlation and mechanism of action between them.

This study aims to systematically investigate the intrinsic relationship between land misallocation and economic resilience and its underlying mechanism. Specifically, the study will proceed in the following steps: (1) empirically test the direct effect of land misallocation on economic resilience; (2) reveal the spatial spillover effects present in this relationship; (3) thoroughly analyze the core mediating pathway through which land misallocation affects economic resilience by hindering technological progress. Addressing the aforementioned three research objectives, specific hypotheses are first derived through theoretical modeling and subsequently subjected to empirical testing. The marginal contributions of this research are twofold. First, it is among the first attempts to integrate land misallocation and economic resilience into a unified analytical framework, empirically examining their causal linkage. Second, it unveils the mechanism through which price distortions in the land factor undermine economic resilience by hindering technological advancement, a pathway empirically substantiated through spatial econometric and mediation models. This theoretical and empirical approach—developed through a model integrating technology and factor allocation and applied to a panel dataset of 95 cities (2012–2024)—enables an in-depth analysis of the pathways through which land misallocation affects firm productivity, factor mobility, and technological progress, while also verifying its spatial spillover effects and nonlinear impacts. Consequently, this research not only addresses a notable gap in the existing literature but also provides a theoretical foundation and policy insights for optimizing land resource allocation and enhancing regional economic resilience.

2. Theoretical Model and Analysis

The genesis of land misallocation stems from local governments, acting as monopolistic suppliers in regional land markets, determining both the quantity and price of land allocated across different industries and uses. Its intrinsic nature manifests as a non-positive correlation between the supply price of land and its marginal return. Specifically, driven by objective functions such as investment promotion or short-term GDP growth, local governments set lower land supply prices for industrial uses characterized by lower factor returns, while assigning higher prices to uses with higher factor returns. This misallocation essentially constitutes a mechanism through which land is supplied at suppressed prices to less efficient industrial uses, effectively functioning as a price subsidy extended by local governments to specific industrial sectors [14]. Such a pricing subsidy mechanism impedes the exit of low-efficiency industries, not only reducing the overall production efficiency of the regional economy [15], but also rendering these industries more vulnerable to exogenous shocks, thereby undermining regional economic resilience.

Subsequently, it is necessary to model the mechanisms and pathways through which land misallocation affects regional economic resilience. Section 2.1 below examines the relationship between land misallocation and economic resilience based on a regional economic production function, while Section 2.2 provides a theoretical analysis of the intrinsic mechanisms by which land misallocation influences regional economic resilience.

2.1. Theoretical Analysis of the Relationship Between Land Misallocation and Economic Resilience

The essence of land misallocation lies in the distorted allocation of production factors. Therefore, it is imperative to first construct a functional model characterizing the relationship between production factors and economic resilience. Economic resilience is defined as the economy’s resistance and recovery capacity in the face of short-term exogenous shocks. While such short-term shocks affect factor returns and total economic output, they evidently do not cause significant changes in the technological level in the short term. Therefore, based on Loupias and Wigniolle (2013), we construct an economic resilience function that includes both technology and production factors [16]:

$$RES=f\left(G\right)=f\left\{{\sum }_i\left[{T_i}^{1-\eta }{\left(K_i^{\alpha }· N_i^{\beta }· L_i^{\gamma }\right)}^{\eta }\right]\right\}$$

(1)

Here, regional economic resilience (RES) is expressed as a function of total regional economic output (G), which is the aggregation of output values of economic entities (firm i) within the region. Assume the output of firm i follows a Cobb–Douglas form, expressed as a function of the average technological level (T) and the production factors: capital (K), labor (N), and land (L). η is the contribution rate of production factors to economic output, hence (1 − η) is the contribution rate of the technological level. α, β, and γ are the elasticity coefficients among the production factors. Economic resilience represents the resistance and recovery capacity of the regional economy to exogenous shocks. Based on Equation (1), if short-term shocks act uniformly on production factors, then the variation in output across firms under risk shocks depends primarily on the technological level. Overall regional economic resilience is the aggregated representation of these firm-level output differences under exogenous shocks. Therefore, regions with advanced technology and a higher contribution rate from technology would exhibit lower fluctuations in economic output, consequently demonstrating higher regional economic resilience—the existing research has also found a significant positive correlation between economic resilience and TFP [17], confirming the above theoretical analysis.

However, using Equation (1) for comparative analysis of economic resilience across regions introduces significant bias. This is because Equation (1) simplifies regional economic output as a functional model of the aggregate firms’ output. In the process of transforming firms’ output into total regional economic output, the technological level cannot be simply arithmetically averaged. That is, differences in technological levels between regions cannot be simply represented by the average difference in firm technological levels. This is because firms with different technological levels have varying market shares in different regions. Therefore, the overall regional technological level requires a weighted average of firm technological levels.

Olley and Pakes (1996) constructed a weighted model of regional economic efficiency level (i.e., technological level) [18]. In this model, if inefficient firms have high market shares, it indicates that production factors are not allocated to the most efficient firms, thereby pulling down the overall regional technological level. Following the Olley–Pakes model, this research also constructs a functional relationship between regional economic resilience (RES) and the weighted regional technological level:

$$RES=f\left(G\right)=f\left({\sum }_i{\theta }_{ij}{T}_i\right)=f\left\{\overline{T}+{\sum }_i\left[\left({\theta }_{ij}-\overline{{\theta }_J}\right) \left(T_i-\overline{T}\right) \right]\right\}$$

(2)

Here, T_i is the productivity (technological level) of firm i, while θ_ij represents the share of land allocated to firm i in industry j within the region. Assuming that labor and capital are freely mobile across regions, the heterogeneity in factor allocation primarily manifests at the level of land. $\overline{T}$ is the simple average productivity of firms in the region, and $\overline{{\theta }_J}$ is the simple average land share of firms in industry j within the region. Therefore, it can be seen that regional economic output is not only related to the average technological level ($\overline{T}$) but also highly correlated with the allocation of land factors among industries with different technological levels $\sum _i\left[\left({\theta }_{ij}-\overline{{\theta }_J}\right) \left(T_i-\overline{T}\right) \right]$. Research on regional economic resilience primarily focuses on the impact of short-term exogenous shocks on the economic level. Therefore, within a short-term horizon, technological levels between regions do not change significantly, making land allocation a key influencing factor for regional economic resilience. The model in Equation (2) reveals that, under the general research assumption of technological neutrality, differences in factor allocation structures between regions affect their respective technological levels, thereby influencing regional economic resilience. Based on the foregoing analysis, the following research hypothesis is proposed:

Hypothesis 1.

Misallocation of land across different industries significantly impairs the performance of regional economic resilience.

2.2. The Underlying Mechanism of Land Misallocation on Economic Resilience

The above model identified the mechanism through which factor allocation affects the technological level and consequently regional economic resilience. Next, the analysis focuses more specifically on the underlying mechanism of land factor allocation on economic resilience. To incorporate land, other production factors, and the technological level into a unified analytical framework, a dual model is constructed based on the analytical conclusion of Hansen and Prescott (2002) that “technological progress in the land factor is much slower than in the capital factor” [19]. Assume there are two firms in the region, A and B: firm A’s primary input factors are land and labor, while firm B’s primary input factors are capital and labor. Consequently, firm B’s technological level leads that of firm A, and the overall technological level of the region depends primarily on firm B’s technological progress.

Based on the above assumptions, the output functions for firms A and B, following the Cobb–Douglas form, are constructed:

$$\left\{\begin{array}{l}{ Y}_A(L,N)=L^{\alpha }{N_A}^{1-\alpha }\\ { Y}_B(K,N)=(TK{)}^{\alpha }{N_B}^{1-\alpha }\end{array}\right.$$

(3)

Here, Y is the output function of the input factors. The total output of firm A (Y_A) is a function of the land factor (L) and the labor factor (N_A). The total output of firm B (Y_B) is a function of the technological level (T), the capital factor (K), and the labor factor (N_B). This research primarily analyses the relationship between the land factor and the technological level, so the labor factor is assumed to be exogenous, with a consistent elasticity coefficient (1 − α) in the output function, and it can flow freely between firms A and B. While the model is built on assumptions of free factor mobility and short-term technological neutrality, which may deviate from reality, these simplifications remain analytically relevant. In China, labor and resources indeed exhibit cross-regional mobility, lending reasonable support to the factor mobility assumption. Furthermore, focusing on factor mobility frictions allows the analysis to concentrate more directly on how land misallocation affects regional technological levels. The assumption of short-term technological neutrality is appropriate given the time span of the panel data and helps isolate the immediate effects of land misallocation. Over the long term, technological progress may occur, which would reinforce the mechanism proposed in this paper but does not undermine the validity of the short-term conclusions.

After defining the output functions in Equation (3), the profit functions are calculated based on the cost of input factors. The labor factor is exogenous, so the labor cost for both firms A and B is the exogenously given per capita income level (W). The land factor cost should be the land supply price. However, due to land price misallocation, the land price allocated to firm A by the local government is lower than the market equilibrium price (p). The existing research models often interpret land price misallocation as a subsidy mechanism [20], so the land factor cost is the difference between the market equilibrium land price (p) and the land price subsidy (ε). The capital factor cost is also exogenous, denoted as r. Based on the above analysis, the profit functions for firms A and B are constructed as follows:

$$\left\{\begin{array}{l}{ \Pi }_A=L^{\alpha }\left[\right(1-\varphi )N{]}^{1-\alpha }-(p-\epsilon )L-W[(1-\varphi )N]\\ { \Pi }_B=\left(TK{)}^{\alpha }\right(\varphi N{)}^{1-\alpha }-rK-W\left(\varphi N\right)\end{array}\right.$$

(4)

Here, Π_A/B is the profit level of firms A and B; N is the total labor factor for firms A and B, and ϕ represents the proportion of the labor factor allocated to firm B, so $N_A=\left(1-\varphi \right)N$ and $N_B=\varphi \mathrm{N}$. This research aims to analyze the underlying mechanism of land price misallocation on the technological level. In Equation (4), ε represents the land misallocation coefficient and T represents the technological level coefficient. Therefore, the focus is on analyzing the correlation function between ε and T. Assuming the total input of production factors between firms A and B is fixed (i.e., L, K, and N in Equation (4) are fixed variables), and given that the labor cost (W) and capital cost (r) are exogenous variables, the main variables affecting the difference in profit levels (Π) between firms A and B are the land factor cost (p − ε) and the allocation ratio of the labor factor (ϕ). Since the labor factor can flow freely between firms A and B, in equilibrium, the per capita profit levels of firm A and firm B should be equal, leading to Equation (5):

$$\left\{\begin{array}{l} \epsilon =p+{\displaystyle \frac{WN(1-\varphi )-L^{\alpha }{N}^{1-\alpha }(1-\varphi {)}^{1-\alpha }}{L}}\\ T={\displaystyle \frac{K(W\varphi N+rK{)}^{\frac{1}{\alpha }}}{(\varphi N{)}^{\frac{1-\alpha }{\alpha }}}}\end{array}\right.$$

(5)

This implies an inverse relationship between the land price misallocation coefficient (ε) and the labor allocation ratio (ϕ). Specifically, subsidies favoring firm A attract labor to that firm, causing an outflow from firm B. Simultaneously, Equation (5) suggests a positive relationship between the labor factor allocation ratio (ϕ) and the technological level coefficient (T). This can be understood as the agglomeration of the labor factor towards the capital-intensive firm B leading to an improvement in the regional technological level. Therefore, the above analysis can be simply deduced as follows: an increase in the land factor price misallocation coefficient (ε) leads to a decrease in the regional technological level coefficient (T). According to Equation (1), the technological level (T) is a positive function of economic resilience (RES). Thus, it indirectly demonstrates that the subsidy mechanism of land prices for inefficient firms reduces the optimization of overall regional production efficiency, thereby negatively impacting overall regional economic resilience. That is, the differentiated land supply strategy adopted by local governments—allocating lower land prices to industries with lower returns—is detrimental to the improvement of regional technological levels and, consequently, undermines regional economic resilience. In light of this, the following research hypothesis is proposed:

Hypothesis 2.

Land misallocation impairs economic resilience by impeding regional technological advancement.

It should be emphasized that the theoretical model assumes firm A to be land-/labor-intensive and firm B to be capital-/labor-intensive—a deliberate simplification designed to clarify the mechanism by which land misallocation affects technological progress through the structure of factor allocation. In reality, Chinese industrial firms often exhibit capital-intensive characteristics. This, however, does not undermine the core logic of the analysis: as long as land misallocation channels resources toward relatively less efficient sectors—even if those sectors also use capital—overall technological progress will be restrained, thereby weakening economic resilience. This simplification aligns with the spirit of the “technology catch-up” framework in Hansen and Prescott (2002) and does not affect the validity of the subsequent empirical tests [19].

3. Research Design and Methodology

3.1. Key Variable Operationalization

To empirically test and analyze the mechanism through which land misallocation affects economic resilience, it is first necessary to operationalize these two primary research variables.

3.1.1. Operationalization of Land Misallocation

Land misallocation refers to the price distortion of land supply across different uses within a region, which severs the relationship between the supply price of the land factor and its factor value, creating a misallocation at the price level. Facing budgetary constraints and fiscal deficit pressures, local governments typically choose to offer substantial price discounts on industrial land in exchange for short-term industrial development, while simultaneously supplying commercial land at higher prices to generate revenue to offset the cost of industrial land price subsidies. Although this price subsidy mechanism for the land factor can alleviate the fiscal constraints of local governments acting as land operators and provide effective growth momentum for regional economic development, this model of using commercial land to “cross-subsidize” industrial land distorts the market price of the land factor and is detrimental to the optimal allocation of land resources from a long-term perspective.

Therefore, the existing studies generally use the degree of relative price distortion between different land uses to measure land misallocation [10]. This approach can reflect both the degree of land factor misallocation and the preferences and tendencies of different local governments in the land market, making it more suitable for the reality of local government monopolistic supply in the land market following the tax-sharing reform. Based on this analysis, the distortive difference between the supply prices of industrial and commercial land is the most effective measure of land misallocation. Consequently, this study selects this most effective measurement model as the research variable for the land misallocation coefficient:

$$Land Misallocation Coefficient={\displaystyle \frac{Average Price of Commercial Land}{Average Price of Industrial Land}}$$

(6)

Equation (6) represents the most commonly adopted proxy variable model in the existing literature for examining land factor misallocation. However, some scholars argue that the supply of industrial land in China, in certain cases, operates through non-market mechanisms, leading to significant deviations between published prices and actual values [21]. Nevertheless, beginning in 2012, official statistics began distinguishing between industrial land supplied via market-based mechanisms and that allocated through non-market means (e.g., administrative allocation). Since the sample period selected for this study commences after 2012, only market-based supply prices are utilized in calculating land prices, thereby mitigating potential biases arising from non-market factors in the research conclusions.

It should be noted that the land misallocation coefficient above, which uses the price ratio between commercial and industrial land, primarily captures price distortions across different land uses. However, land misallocation may also manifest as spatial mismatch of land resources (e.g., a disconnect between land supply and industrial layout) and within-use demand mismatch (i.e., misallocation among different firms within the same land-use category). Due to limitations in data availability, this study focuses on inter-use price distortion—the most typical and measurable form of land misallocation. Future research could extend the analysis to other dimensions.

3.1.2. Operationalization of Economic Resilience

Currently, in theoretical circles, measurement models for regional economic resilience are mainly divided into two types: Multi-dimensional Indicator System Evaluation Models and Single-variable Shock Econometric Models. First, from the perspective of Multi-dimensional Indicator System Evaluation Models, economic resilience is a composite concept encompassing economic vulnerability, reorganization capacity, and adaptive capacity. For example, Brakman et al. (2015) used GDP, social capital accumulation, and non-economic indicators such as population density, ecological environment, public services, and infrastructure to measure urban economic resilience levels [22]. However, due to the inherent subjectivity in indicator selection and the significant variation in weights assigned to different indicators within the resilience evaluation system, the evaluation results of different measurement models cannot be compared horizontally, leading to their infrequent use in empirical research. Second, from the perspective of Single-variable Shock Econometric Models, economic resilience is viewed as the fluctuation of one or more regional economic variables in the face of exogenous shocks. For instance, selecting economic output or employment rate as the single variable and using the amplitude of its fluctuation during an exogenous shock as the measure of regional economic resilience. This Single-variable Shock Econometric Model effectively overcomes the shortcomings of Multi-dimensional Indicator System Evaluation Models and is increasingly adopted in complex empirical analyses.

To facilitate a comparative analysis between the empirical findings of this paper and existing research outcomes, this study adopts the Single-variable Shock Econometric Model as the measurement tool for economic resilience. This method calculates a counterfactual regional economic level using the growth rate of the overall economic variable during a shock. Regional resilience is then determined by comparing the actual growth rate with this counterfactual rate. If the actual value is higher than the counterfactual level, it indicates stronger regional economic resilience; conversely, if the actual economic level is lower than the counterfactual level, it indicates relatively weaker regional economic resilience. Martin (2012) first used this counterfactual level method for measuring economic resilience [23]: RES_i = (ΔG_i/G_i)/(ΔG_N/G_N), where G is the single economic variable (employment rate initially, later changed to economic output), i denotes the region, and N denotes the nation. Thus, regional economic resilience is expressed as the relative change rate of the regional economic fluctuation compared to the national economic fluctuation during the risk shock period. This research appropriately modifies the aforementioned model to reconstruct the regional economic resilience measurement model:

$$\left\{\begin{array}{l} RE{S}_i^t={\displaystyle \frac{[\Delta G_i^t-(\Delta G_i^t{)}^{exp}]}{\left|(\Delta G_i^t{)}^{exp}\right|}}\\ (\Delta G_i^t{)}^{exp}=G_i^t\times (g_N^t/g_N^{t-1})\times g_i^{t-1}\end{array}\right.$$

(7)

In Equation (7), $RE{S}_i^t$ represents the regional economic resilience of city i in period t. It is defined as the relative ratio between the actual economic level Δ$G_i^t$ of city i and the expected counterfactual level (Δ$G_i^t$)^exp: where G represents GDP, g_N represents the national average GDP growth rate, and g_i represents the GDP growth rate of city i. When an exogenous risk shock occurs, if the decline in the economic growth rate of city i is smaller than the decline in the national average economic growth rate, then $RE{S}_i^t$ > 0 in Equation (7) indicates relatively stronger economic resilience in that region.

The regional economic resilience examined in this study primarily encompasses two dimensions: resistance and recovery. Resistance refers to the stability of a regional economy in the face of risk shocks, whereas recovery captures the extent and speed of economic rebound toward the pre-shock growth trajectory following such disturbances. The speed of regional economic recovery is typically applied when assessing resilience to a single specific shock—such as a financial crisis or pandemic. In contrast, this study employs annual panel data, treating each year as an observation unit potentially subject to various unexpected shocks. Resilience is captured by measuring the deviation of actual regional fluctuations from the national trend. The above model proposed by Martin (2012) [23], which is well-suited for capturing regional economic resistance and recovery under multi-period, multi-source shocks, has been widely adopted in resilience assessments across countries. Moreover, this model has been repeatedly employed in empirical examinations of China’s economic resilience [24,25]. By continuing to adopt this model for measuring regional economic resilience, the present study facilitates cross-sectional comparative analysis of research findings.

3.2. Research Methods

To test the impact of land misallocation on economic resilience, a linear regression empirical model needs to be constructed. However, land factor allocation may exhibit spatial autocorrelation across different regions. To address potential biases arising from spatial dependencies in traditional linear regression models, this research employs a spatial econometric model. Furthermore, to examine the mechanism through which land misallocation affects economic resilience, the mediation model proposed by Baron and Kenny (1986) will be utilized for empirical analysis [26].

3.2.1. Spatial Econometric Model

The foundation of the spatial econometric model is constructing a reasonable spatial weight matrix. However, in the subsequent empirical section (Section 4), due to data availability constraints for land misallocation, the sample consists of 95 Chinese cities (including 4 ultra-large municipalities). Most sample cities are not geographically adjacent, making it impossible to use a traditional weight matrix based on geographical contiguity. This study investigates the mechanism through which land misallocation affects economic resilience by influencing the level of technological advancement. Since technological diffusion tends to occur more readily between cities with comparable economic development levels—reflecting similar knowledge absorption capacities and industrial structures—and because cities of similar economic scale generally face analogous exposure to shocks and policy environments, this study, drawing on established research methodologies [27], employs a spatial economic weight matrix constructed based on inter-city GDP disparities to better capture the relationship between technological spillovers and economic resilience interactions among cities. The matrix is formulated as follows:

$$W_{ij}=\left\{\begin{array}{l} 1/\left(d_{ij}\times \left|GD{P}_i-GD{P}_j\right|\right)\left(\begin{array}{cc}if& i\ne j\end{array}\right)\\ 0\left(\begin{array}{cc}if& i=j\end{array}\right)\end{array}\right.$$

(8)

In Equation (8), W_ij represents the spatial economic weight matrix, defined as the reciprocal of the product of the absolute difference between the total economic output (GDP) of city i and city j and the geographical distance (d_ij). This spatial economic weight matrix accounts for both economic disparities and geographical distances between sample cities: when two sample cities are economically similar and geographically proximate, they receive a higher spatial weight.

Once the spatial weight matrix is determined, Moran’s I needs to be calculated to test for spatial autocorrelation of the core variables. If spatial autocorrelation exists, a spatial econometric model should be constructed as follows:

$$\begin{gathered} RE{S}_{i,t}={\beta }_0+\rho \times \sum _{j=1}^n{W}_{i,j}·RE{S}_{j,t}+{\beta }_1\times LM{C}_{it}+{\beta }_2\times Control{s}_{it} \\ +{\theta }_1\times {\sum }_{j=1}^n{W}_{i,j}·LM{C}_{j,t}+{\theta }_2\times {\sum }_{j=1}^n{W}_{i,j}·Control{s}_{j,t}+\epsilon \end{gathered}$$

(9)

In Equation (9), RES represents regional economic resilience, LMC is the land misallocation coefficient, Controls represents a series of control variables, W_ij is the spatial economic weight matrix, and ε is the error term. Additionally, ρ is the coefficient of the spatial lag term of the dependent variable, and θ is the coefficient of the spatial interaction term of the independent variables.

Next, it is necessary to sequentially test the spatial econometric model specified in Equation (9) following the order: OLS → SEM → SAR → SDM. When ρ = 0 and all θ_i = 0 (i = 1, 2, …), it indicates no spatial correlation among all variables, and Equation (9) becomes the classic OLS model. When the regression coefficients β_i of the independent variables, their interaction term coefficients θ_i and the dependent variable spatial lag term coefficient ρ satisfy θ_i = −ρβ_i, it indicates that the spatial effect transmission occurs mainly through the error term, and Equation (9) becomes the Spatial Error Model (SEM). When ρ ≠ 0 but all θ_i = 0, the spatial interaction of independent variables does not exist, and only unidirectional spatial correlation of the dependent variable exists between regions; then Equation (9) becomes the Spatial Autoregressive Model (SARM). When ρ ≠ 0 and θ_i ≠ 0, it indicates the simultaneous existence of spatial correlation effects from the dependent variable and spatial interaction effects from the independent variables; then Equation (9) is the Spatial Durbin Model (SDM).

The control variables in the aforementioned spatial econometric model include the following: (I) Degree of Openness (Open): The ratio of foreign investment to total economic output. Regions with a higher proportion of foreign investment are more susceptible to exogenous risk shocks, which may subsequently impact regional economic resilience. (II) Urbanization Rate (Urban): The proportion of urban population to the city’s permanent residents. Urbanization promotes the agglomeration of production factors, thereby exerting a significant influence on regional economic resilience. (III) Income Index (Income): Per capita disposable income of urban residents. The existing studies have found that regions with higher per capita income tend to exhibit stronger economic resilience [24]. (IV) Market Scale (Market): Total retail sales of consumer goods. Research indicates that the expansion of regional consumer markets can enhance the economy’s capacity to withstand risk shocks [28]. Both the Income and Market control variables are logarithmically transformed in the empirical analysis.

3.2.2. Mediation Model

If the spatial econometric model confirms that land misallocation has a significant impact on economic resilience, the mechanism between them needs further testing and analysis. The theoretical research section (Section 2) found that land misallocation affects regional economic resilience by influencing the technological level. The analysis of such underlying mechanism is typically empirically tested using the mediation model. First, the total effect of land misallocation on economic resilience is tested. Second, the significance of the relationship between the land misallocation coefficient and the mediating variable is examined. Finally, the mediating variable is incorporated into the total effect model to assess the significance of the mediating effect. Given the spatial autocorrelation between regional economic resilience and land misallocation, the mediation effect test must also employ a spatial econometric model, which can be briefly represented as follows:

$$\left\{\begin{array}{l}\begin{array}{cc}Function1:& RE{S}_{i,t}={\rho }_1\times W_{i,j}RE{S}_{j,t}+\alpha \times LMC+{\theta }_1\times W_{i,j}IndVariable{s}_{j,t}+{\epsilon }_1\end{array}\\ \begin{array}{cc}Function2:& M_{i,t}={\rho }_2\times W_{i,j}{M}_{j,t}+\beta \times LMC+{\theta }_2\times W_{i,j}IndVariable{s}_{j,t}+{\epsilon }_2\end{array}\\ \begin{array}{cc}Function3:& RE{S}_{i,t}={\rho }_3\times W_{i,j}RE{S}_{j,t}+\alpha \prime \times LMC+\lambda \times M_{i,t}+{\theta }_3\times W_{i,j}IndVariable{s}_{j,t}+{\epsilon }_3\end{array}\end{array}\right.$$

(10)

In the three equations of (10), RES represents regional economic resilience as the dependent variable, LMC the land misallocation coefficient as the core independent variable, M is the mediating variable (proxy for technological level), W is the spatial economic weight matrix, and W_i,j IndVariables_j,t represents the spatial interaction terms for all independent variables.

Step 1: Calculate the regression coefficient α in Function 1, which represents the total effect of the core independent variable LMC on the dependent variable RES. If α is not statistically significant, the mediation effect does not hold.

Step 2: Calculate the regression coefficient β in Function 2 to test whether the core explanatory variable LMC has a significant impact on the mediating variable M.

Step 3: Calculate the regression coefficients α′ and λ in Function 3. If both β from Function 2 and λ from Function 3 are significant, it indicates that the mediation effect exists. If α′ in Function 3 is also significant, it indicates a partial mediation effect. If α′ is not significant, it indicates a complete mediation effect. However, if either β or λ is not significant, the Sobel test is needed to determine the significance of the mediation effect. When the mediation effect exists, the ratio (β·λ/α) is referred to as the proportion of the mediation effect [27].

4. Empirical Analysis

4.1. Baseline Tests

The empirical analysis employs a panel dataset covering 95 Chinese cities (including 91 prefecture-level cities and 4 municipalities directly under the central government) from 2012 to 2024. This sample is selected for the following reasons: first, most Chinese cities only report average land supply prices in their statistical records, while these 95 cities specifically disclose supply prices for different land uses; second, pre-2012 statistics on industrial land supply prices include non-market allocation methods such as administrative grants. To investigate land misallocation across different uses and eliminate interference from non-market factors, this study adopts the aforementioned panel data as the empirical sample. Among these sample cities, the proportions of eastern developed cities and non-eastern cities are roughly equal, ensuring representativeness from a regional distribution perspective. Furthermore, these cities account for nearly 70% of China’s non-agricultural GDP, making them representative in terms of economic scale.

Data for economic resilience and land misallocation are calculated based on publicly available information from the China Land and Resources Statistical Yearbook and the Wind database. Data for other variables—including foreign direct investment, urbanization rate, per capita disposable income, total retail sales of consumer goods, and the number of patent grants—are also sourced from the Wind database. To mitigate biases caused by heteroscedasticity and outliers in the empirical results, non-proportional variables are logarithmically transformed, and all samples undergo winsorization.

Before conducting the empirical analysis, the spatial autocorrelation of the explained variable needs to be tested. Using the spatial economic weight matrix W_ij constructed in Equation (8) above, Moran’s I is calculated as follows:

$$\begin{array}{cc}Moran\mathrm{’}s& I\end{array}={\displaystyle \frac{{\sum }_{i=1}^n{\sum }_{j=1}^n{W}_{ij}\left(V_i-\overline{V}\right)\left(V_j-\overline{V}\right)}{S^2· {\sum }_{i=1}^n{\sum }_{j=1}^n{W}_{ij}}}$$

(11)

In Equation (11), S² represents the sample variance, n is the total number of samples, and V represents the explained variable being tested for spatial autocorrelation. The test results of Moran’s I indicate that regional economic resilience (RES), the land misallocation coefficient (LMC), and all mediating variables exhibit spatial autocorrelation, a spatial econometric model should be used for regression testing. Given that the empirical test sample is panel data, stationarity tests for the main variables are required before spatial econometric regression. Panel unit root tests using the LLC and IPS methods showed that some variables are non-stationary. However, the results of Kao and Pedroni cointegration tests for the research model indicate that the model variables satisfy a cointegration relationship, allowing for the establishment of a panel regression model.

Subsequently, the optimal spatial econometric model is selected sequentially following the order: OLS → SEM → SAR → SDM.

First, Lagrange Multiplier (LM) tests are conducted to determine the presence of spatial correlation in the model. The LM test statistics reported in the first row of

Table 1. Model Specification Tests for Spatial Econometric Models.

Test Method	Test Statistic	Statistic Value	p-Value
LM Test	Moran’s I	0.530	0.406
	Robust LM-lag	0.859 ***	0.004
	Robust LM-error	0.772 ***	0.007
LR Test	LR-SDM/SEM	3.56 **	0.045
	LR-SDM/SARM	3.48 *	0.068
Wald Test	Wald-SDM/SEM	3.48 ***	0.029
	Wald-SDM/SARM	3.54 **	0.047

Note: ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

are all significant at the 1% level, indicating that spatial econometric models are appropriate for regression analysis. Based on the Hausman test, a fixed-effects model is selected.

Second, Likelihood Ratio (LR) tests are performed for the SEM, SAR, and SDM models. The LR test statistics in the second row of Table 1 are all significant at the 10% level, leading to the rejection of the null hypothesis and confirming that the SDM model is superior to both SEM and SAR.

Third, Wald tests are conducted to examine the stability of the SDM model. The Wald test statistics in the third row of Table 1 are also significant at the 5% level, indicating that the SDM model cannot be simplified to either SEM or SAR.

Although the tests indicate that the SDM model is the optimal choice,

Table 2. Empirical Results.

Variables	(1) OLS	(2) SEM	(3) SARM	(4) SDM	(5) Dynamic-SDM
L.RES					−0.141 ** (0.132)
LMC	−0.006 *** (0.002)	−0.006 * (0.005)	−0.007 *** (0.004)	−0.005 *** (0.004)	−0.008 ** (0.004)
Open	−0.003 (0.003)	−0.003 (0.004)	−0.003 (0.003)	−0.001 (0.005)	0.003 (0.014)
Urban	1.676 * (1.386)	1.717 * (1.383)	5.422 * (4.971)	1.704 * (1.387)	4.281 (4.119)
lnIncome	0.791 *** (0.224)	0.733 (0.580)	2.603 ** (1.088)	−0.258 (0.677)	1.594 ** (1.198)
lnMarket	−0.536 *** (0.208)	−0.530 ** (1.122)	−2.175 *** (0.937)	−0.509 *** (0.192)	−2.312 *** (1.310)
W. RES			0.047 ** (0.040)	0.049 ** (0.072)	0.041 ** (0.040)
W. LMC				0.014 * (0.013)	0.017 * (0.020)
Log-L		−225.821	−220.653	−194.042	−210.908
R2	0.112	0.346	0.392	0.504	0.496

Note: ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. Standard errors are indicated in parentheses.

still reports the empirical results from various econometric models for comparative analysis. Column (1) reports the results of the non-spatial OLS regression, showing that the coefficient for the core explanatory variable, the land misallocation coefficient (LMC), is significantly negative. However, the low R² of the econometric model indicates weak explanatory power. Columns (2) to (4) report the spatial econometric regression results for SEM, SARM, and SDM, respectively. Based on the LR and Wald tests, the SDM model is selected as the optimal choice. Since economic resilience not only has spatial correlation but might also be influenced by its own past values in the time dimension, Column (5) reports the test results for a Dynamic Spatial Durbin Model that includes the one-period lagged term of the dependent variable (RES).

The results in Table 2 show that all econometric models indicate a significant negative effect of the land misallocation coefficient (LMC) on regional economic resilience (RES). That is, cities with more severe distortions in land supply prices have relatively lower economic resilience. This is consistent with the conclusions of the theoretical analysis: when local governments supply industrial land at lower prices and commercial land at higher prices, this non-market-based, differentiated land supply price misallocation reduces the region’s economic resilience.

Furthermore, comparing the goodness-of-fit of the several spatial econometric models using the Log-Likelihood (Log-L) and R-squared values shows that the SDM model has a better fit and relatively stronger explanatory power. The static Spatial Durbin Model also has more significant regression coefficients compared to the dynamic version. Therefore, the results of the static SDM model presented in Column (4) are ultimately selected as the basis for empirical analysis. In this model, not only is the coefficient for the land misallocation coefficient (LMC) significant, but its spatial interaction term (W × LMC) is also significant, yet the signs of the two are completely opposite. This indicates that urban economic resilience is not only negatively affected by land misallocation within its own region but also potentially positively affected by land misallocation in economically similar cities. This can be understood as the “industrial crowding-out” effect of land misallocation: distorted allocation of the land factor crowds out high-quality industries from the local region, adversely affecting the local economic resilience. However, when these industries relocate to economically similar regions, it may conversely enhance the economic resilience of those other regions. A deeper understanding of this spatial spillover effect of land misallocation suggests a potential risk of “beggar-thy-neighbor” policy competition among regions. If cities competitively lower industrial land prices to attract investment, such practices may induce short-term industrial relocation but will likely reduce overall resource allocation efficiency in the long run and exacerbate regional development imbalances. This insight underscores the importance of cross-regional coordination in land policy and the establishment of a sound competitive mechanism.

For the spatial spillover effects of the explanatory variables, the partial differential method of Lesage and Pace (2014) can be used to decompose the impact of independent variables on the dependent variable in the spatial econometric model into direct effects, indirect effects, and total effects [29]. The direct effect reflects the average impact of an independent variable on the dependent variable within the same region. The indirect effect (also known as the spatial spillover effect) reflects the impact of an independent variable on the dependent variable in other regions. The total effect expresses the average impact of an independent variable on the dependent variable across all regions.

Table 3. Direct, Indirect, and Total Effects of the SDM.

Variables	Direct Effect	Indirect Effect	Total Effect
LMC	−0.005 *** (0.004)	0.014 * (0.014)	−0.008 *** (0.005)
Open	−0.001 (0.002)	−0.004 (0.014)	−0.003 (0.015)
Urban	1.855 ** (1.334)	0.352 (0.566)	2.207 * (1.691)
lnIncome	0.237 (0.683)	1.435 ** (1.045)	1.673 ** (1.131)
lnMarket	−0.518 *** (0.188)	−0.421 ** (0.172)	−0.939 ** (0.711)

Note: ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. Standard errors are indicated in parentheses.

reports the measurement results for the direct effects, spatial spillover effects, and total effects of each independent variable. The spatial spillover effect coefficient for LMC is significantly positive at the 10% level, indicating that land misallocation in a given region has a certain positive effect on the economic resilience of other economically similar regions. Meanwhile, the total effect coefficient for LMC is significantly negative, indicating that land misallocation has an adverse effect on the economic resilience of the overall region.

The aforementioned empirical results validate Hypothesis 1 proposed in the theoretical analysis: land misallocation undermines regional economic resilience. Furthermore, the empirical findings supplement that the negative impact of land misallocation on economic resilience exhibits spatial spillover effects.

4.2. Robustness Tests

4.2.1. Endogeneity Treatment

The essence of economic resilience is the relative differential state of regional economic fluctuations in the face of exogenous shocks, and these differences might also influence local government decisions regarding land supply, potentially leading to endogeneity issues in the econometric model. To mitigate potential endogeneity bias affecting the estimation results of the baseline tests, this study employs a System-Generalized Method of Moments (SYS-GMM) and an Instrumental Variables (IV) approach for robustness tests.

First, drawing on the classical methodology of Arellano and Bond [30], a System-Generalized Method of Moments (SYS-GMM) model is constructed. By incorporating the first-order lag of the dependent variable, this model effectively addresses potential endogeneity issues inherent in dynamic panel data. However, traditional GMM estimation is primarily applied in non-spatial linear regression models. To simultaneously control for endogeneity and spatial correlation, Han and Phillips (2006) combined the Dynamic Spatial Autoregressive Model (DSARM) with System GMM (implemented in Stata 15.0 using the spregdpd command) [31]. This spatial GMM method was later extended to the Spatial Durbin Model. Columns (1) and (2) of

Table 4. Results of Robustness Tests for Endogeneity Treatment.

Variables	(1) SYS-GMM	(2) Han-Phillips GMM	(3) IV 2SLS
L.RES	−0.094 *** (0.002)	−0.088 *** (0.033)	−0.074 ** (0.029)
LMC	−0.017 ** (0.013)	−0.009 * (0.008)	−0.004 ** (0.003)
IV (the first-stage)			−0.209 ** (0.110)
Open	−0.005 (0.003)	0.002 (0.014)	−0.001 (0.004)
Urban	10.052 *** (2.141)	8.880 *** (13.611)	1.814 * (1.097)
lnIncome	2.308 *** (0.481)	2.260 (2.517)	0.260 ** (0.073)
lnMarket	−3.577 *** (0.134)	−3.466 *** (1.235)	−0.510 *** (0.192)
W. LMC		0.001 (0.000)	−0.010 *** (0.008)
R2			0.418
LM/Wald			70.598 ***/115.455 **
Sargan [P]	37.333 [0.318]	92.800 [0.211]

Note: ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. Standard errors are indicated in parentheses.

present the test results from these two GMM approaches.

Secondly, the instrumental variable for industrial land supply prices is constructed using urban topographic slope and plot floor area ratio. The existing studies have found that the supply price of industrial land is significantly negatively correlated with urban topographic slope and significantly positively correlated with plot floor area ratio [32]. This instrumental variable satisfies both the relevance condition and the exogeneity requirement. Topographic slope is naturally formed and unrelated to other economic variables, meeting the exclusion restriction. However, since topographic slope is a non-time-series variable, it is multiplied by the inverse of the regional average floor area ratio to form a panel instrumental variable. The regional land floor area ratio is determined by the central government based on various factors such as transportation and environmental protection, rather than being adjusted dynamically by local governments in real time. The regional average floor area ratio is calculated as the weighted average of the floor area ratios of individual industrial land plots, weighted by plot area. The product of topographic slope and floor area ratio has been widely adopted as an instrumental variable in the literature on land misallocation [6,7]. This choice is justified because it directly influences land development costs and, consequently, land prices—satisfying the relevance condition. Moreover, being naturally formed and uncorrelated with regional economic indicators, it also meets the exogeneity requirement. Column (3) of Table 4 presents the test results using the 2SLS method for this instrumental variable.

Columns (1) and (3) in Table 4 report the test results for System GMM and Dynamic Spatial Durbin GMM (Han-Phillips GMM), respectively. The Hansen test indicates the validity of the instruments. The results from both testing methods show that the coefficient for LMC is significantly negative, supporting the conclusion from the baseline tests that land misallocation has a negative impact on regional economic resilience. Column (3) of Table 4 presents the test results from the instrumental variables approach. The regression coefficient of the instrumental variable in the first stage is statistically significant, and both the LM and Wald statistics indicate no issues of under-identification or weak instruments. The second-stage regression results continue to demonstrate a significantly negative effect of land misallocation on regional economic resilience. Therefore, after addressing endogeneity concerns through the System-Generalized Method of Moments and the instrumental variables method, the findings of the baseline tests remain robust.

4.2.2. Variable Refinement

Although the regional economic resilience variable (RES) employed in the baseline test is widely adopted in the existing literature [23], recent studies have highlighted that this measurement model fails to adequately account for the influence of heterogeneity in economic structures across regions on economic resilience [33]. Specifically, consider two cities, A and B, both exposed to an exogenous shock that primarily impacts the tertiary sector. While the volatility of the tertiary sector in City B during the shock period is significantly higher than in City A—theoretically indicating that City A possesses stronger inherent capacity to withstand shocks—the tertiary sector constitutes a substantially larger share of the economic structure in City A compared to City B. Consequently, the counterfactual-based measurement model may yield a lower economic resilience score for City A than for City B. This suggests that differences in industrial structure across regions can introduce systematic bias into the measurement of economic resilience. Therefore, it is necessary to control for the effects of industrial structure heterogeneity in the model to enhance the validity of the estimates.

To account for the heterogeneous contributions of industrial structure to economic growth, this study constructs an industrial structure index d_Nj, defined as the ratio of the national average growth rate of industry $j$ (g_N,j) to the national average GDP growth rate (g_N):

$$d_{Nt}=g_{Nj}/g_N$$

(12)

Incorporating this industrial structure index into Equation (7), the sectoral resilience (SRES) measure is formulated as follows:

$$SRE{S}_{\mathrm{i},\mathrm{j}}^t={\displaystyle \frac{G_{i,j}^t \times g_{i,j}^t-[G_{i,j}^t \times (g_N^t/g_N^{t-1})\times d_{N,j}\times g_{i,j}^{t-1}]}{\left|G_{i,j}^t\times \left(g_N^t/g_N^{t-1}\right)\times d_{N,j}\times g_{i,j}^{t-1}\right|}}$$

(13)

where ${SRES}_{i,j}^t$ represents the resilience level of industry j in city i during period t. This indicator is determined by the actual economic output change of the industry $(G_{i,j}^t\times g_{i,j}^t)$ and the counterfactual output level adjusted for industrial structure $[G_{i,j}^t\times (g_N^t/g_N^{t-1})\times d_{N,j}\times g_{i,j}^{t-1}]$. The counterfactual level is constructed based on the national average growth rate of industry j (g_N,j), thereby controlling for the structural influence of national industrial growth trends on the measurement of regional resilience.

Following the model specified in Equation (13), the resilience of the secondary industry and that of the tertiary industry are, respectively, calculated and employed as dependent variables in regression analyses.

Table 5. Results of Robustness Test Using Sectoral Resilience as the Refined Variable.

Variables	Resilience of the Secondary Industry		Resilience of the Tertiary Industry
	(1) OLS	(2) SDM	(3) OLS	(4) SDM
LMC	−0.005 *** (0.002)	−0.007 ** (0.004)	−0.006 *** (0.002)	−0.008 * (0.004)
Open	−0.002 (0.003)	−0.003 (0.005)	−0.013 (0.025)	−0.002 (0.004)
Urban	1.850 * (1.020)	5.015 * (4.916)	2.011 * (1.165)	4.556 * (4.038)
lnIncome	0.698 *** (0.236)	0.976 * (0.771)	0.780 *** (0.235)	1.277 * (1.079)
lnMarket	−0.507 ** (0.202)	−2.378 ** (1.080)	−0.496 ** (0.204)	−2.254 ** (1.023)
W. RES		0.031 * (0.027)		0.030 ** (0.051)
W. LMC		0.018 * (0.013)		0.019 ** (0.013)
Log-L		−217.574		−216.615
R2	0.315	0.578	0.353	0.493

Note: ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. Standard errors are indicated in parentheses.

presents the estimation results after variable refinement: columns (1) and (2) report the regression results for secondary industry resilience, while columns (3) and (4) report those for tertiary industry resilience. The results indicate that the coefficient for land misallocation (LMC) remains significantly negative across all specifications, demonstrating that even after controlling for the influence of interregional industrial structure differences on the measurement of economic resilience, land misallocation continues to exert a significant inhibitory effect on regional economic resilience. This outcome further corroborates the robustness of the findings in this study.

In addition to addressing endogeneity in the baseline estimates through the System-Generalized Method of Moments (SYS-GMM) and the Instrumental Variables (IV) approach, and to controlling for structural bias through variable refinement, this study also conducted robustness tests for potential bias in spatial matrix specification. Re-estimation using an economic weight matrix based solely on GDP similarity, excluding geographical distance, yielded findings consistent with the baseline results, confirming that the empirical conclusions are not driven by model specification bias (see Appendix B for detailed results). By systematically controlling for issues related to endogeneity, structural bias, and matrix specification, the core finding of this study—that land misallocation significantly inhibits economic resilience—remains robust throughout, thereby strengthening the credibility and applicability of the research findings.

4.3. Empirical Examination of the Underlying Mechanism

Theoretical research found that land misallocation significantly affects regional economic resilience through the “technological level”. Price misallocation in land supply across different uses delays the exit of technologically backward enterprises and hinders the flow of production factors from low-efficiency to high-efficiency sectors. This manifests as land misallocation impeding regional technological development, thereby negatively impacting economic resilience. Next, a mediation model is constructed to empirically test whether the “technological level” is the pathway through which land misallocation affects economic resilience.

The existing studies predominantly measure regional technological levels from the perspective of technological output, commonly using the “number of patents granted” as a proxy variable. Although the number of patents does not fully equate to productivity improvement—a limitation of this proxy variable—some studies also use TFP to measure regional technological levels. However, extensive empirical research shows a significant positive correlation between TFP and patent counts in China, leading to the widespread adoption of patent numbers as a proxy in the literature [34,35]. To facilitate cross-study comparability, following the above research convention, this study also adopts the total number of patents granted (denoted as Patent) as a preliminary indicator of regional technological capability. However, China’s current patent system includes three types: invention patents, utility model patents, and design patents, which differ significantly in their technical substance and innovative quality. Among these, utility model and design patents have lower technical thresholds and are relatively easier to authorize, and thus may not accurately reflect a region’s core technological capacity. Including them may introduce measurement bias in assessing technological levels [35]. Therefore, it is necessary to distinguish between these patent types in subsequent analyses.

Accordingly, this study proceeds in two steps: First, the total number of patents granted (Patent) is used as a proxy for technological level and examined within a mediation model, with results reported in

Table 6. Empirical Results of Mediation Tests.

Variables	(1) RES	(2) lnPatent	(3) RES
LMC	−0.005 *** (0.004)	−0.002 * (0.000)	−0.006 *** (0.002)
lnPatent			0.633 (0.945)
Open	−0.001 (0.005)	0.002 ** (0.001)	−0.002 (0.094)
Urban	1.704 * (1.387)	3.093 *** (1.052)	1.628 * (1.012)
lnIncome	−0.258 (0.677)	1.639 *** (0.288)	0.282 (0.306)
lnMarket	−0.509 *** (0.192)	0.054 *** (0.156)	−0.585 *** (0.204)
W. RES	0.049 ** (0.072)		0.047 ** (0.039)
W. lnPatent		0.358 *** (0.069)	−0.395 (0.599)
W. LMC	0.014 * (0.013)	0.752 ** (0.407)	0.014 * (0.012)
R2	0.504	0.731	0.312
Sobel Z [p Value]			0.652 [0.514]

Note: ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. Standard errors are indicated in parentheses.

. Second, patents are disaggregated into high-level invention patents and other lower-level patents (utility models and designs), which are then separately incorporated into the model for further testing. This step aims to further elucidate the validity and heterogeneity of the technological transmission mechanism, with corresponding results presented in

Table 7. Mediation Tests with Disaggregated Patent Variables.

Variables	(1) RES	(3) lnInvention	(3) RES	(4) lnDesign	(3) RES
LMC	−0.005 *** (0.004)	−0.004 * (0.004)	−0.007 *** (0.002)	0.002 (0.001)	−0.005 *** (0.002)
lnInvention			0.063 ** (0.055)
lnDesign					−0.106 (0.095)
Open	−0.001 (0.005)	0.003 (0.002)	−0.001 (0.003)	0.001 (0.001)	−0.001 (0.003)
Urban	1.704 * (1.387)	2.969 ** (1.202)	1.6524 * (0.992)	2.959 *** (0.985)	1.997 * (1.091)
lnIncome	0.258 (0.677)	0.891 *** (0.598)	0.232 (0.298)	0.503 ** (0.446)	0.409 ** (0.303)
lnMarket	−0.509 *** (0.192)	0.588 *** (0.186)	−0.584 *** (0.214)	0.677 *** (0.121)	−0.417 ** (0.164)
W. RES	0.049 ** (0.072)		0.048 ** (0.039)		0.048 * (0.038)
W. lnInvention		0.053 *** (0.037)	−0.182 ** (0.076)
W. lnDesign				0.172 ** (0.073)	−0.133 (0.381)
W. LMC	0.014 * (0.013)	0.005 *** (0.001)	0.014 ** (0.013)	0.002 (0.001)	0.014 (0.013)
R2	0.504	0.656	0.390	0.356	0.185
Sobel Z [p Value]			1.795 [0.073]		0.345 [0.730]

Note: ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. Standard errors are indicated in parentheses.

.

First, following the setting in the existing research [34], the number of patents granted (Patent) is selected as the proxy variable for the regional technological level. The mediation effect test results are reported in Table 6. The results in column (2) show that the coefficient of LMC on lnPatent is significantly negative, indicating that land misallocation hinders the progress of the regional technological level, verifying the research hypothesis proposed in the theoretical analysis.

However, the results in column (3) of Table 7 show that the coefficient for lnPatent is not significant, failing to prove that lnPatent can serve as a mediating variable through which land misallocation affects economic resilience. The empirical results ultimately also failed the Sobel test, indicating that the mediation effect using the number of patents granted (Patent) is not established. This conclusion should not be simplistically interpreted as land misallocation being unable to undermine economic resilience by affecting regional “technological level.” A potential underlying reason may stem from measurement bias in the proxy variable—namely, using the total number of patents granted (Patent) as an indicator of regional technological level may inadequately capture the actual structure of technological capabilities.

Following the patent weighting criteria established by Zhang et al. (2023) [35], invention patents are classified as high-weight patents (denoted as Invention) due to their higher technical requirements and greater difficulty in obtaining authorization. In contrast, utility model and design patents are categorized as low-weight patents (denoted as Design) because of their relatively lower technical content and easier acquisition process. The number of patents granted is then disaggregated by high and low weights, and the mediation test is conducted again. The results are presented in Table 7.

Columns (1) to (3) in Table 7 report the test results using invention patents (Invention) as the mediating variable. Columns (4) to (6) report the results using utility model and design patents (Design) as the mediating variable. Using Design as the mediating variable failed the Sobel test, indicating the mediation effect is not established. However, in column (2), the coefficient of the land misallocation coefficient (LMC) on the number of invention patents granted (Invention) is significantly negative, again proving the negative effect of land misallocation on the regional technological level. In column (3), both the coefficients for LMC and Invention are significant and passed the Sobel test, indicating a partial mediation effect for Invention. Since invention patents (Invention) require a higher technological level, they serve as a better proxy for “technological level” compared to the total number of patents granted (Patent).

The above empirical test results confirm the Hypothesis 2 proposed by the theoretical study: land misallocation reduces regional economic resilience by hindering the progress of the regional technological level.

5. Conclusions and Policy Implications

Through theoretical analysis and empirical testing, this research systematically investigates the underlying mechanism of land misallocation on economic resilience, arriving at the following core conclusions:

(1).

Land misallocation significantly inhibits regional economic resilience. The research finds that the “Price Scissors Gap” strategy employed by local governments—supplying industrial land at lower prices and commercial land at higher prices—leads to land factor misallocation, thereby reducing regional economic resilience. Simultaneously, the negative impact of land misallocation on regional economic resilience exhibits spatial spillover effects.

(2).

Land misallocation indirectly weakens economic resilience by hindering technological progress. Using invention patents as the core proxy for regional technological level and employing the mediation model for empirical testing reveals that land misallocation significantly inhibits regional technological progress and further weakens economic resilience. This indicates that land misallocation, by “crowding out” efficient enterprises and delaying the exit of inefficient ones, hinders the flow of production factors to high-productivity sectors, ultimately reducing the region’s capacity to cope with shocks.

(3).

The agglomeration effect induced by land misallocation is characterized by “diseconomies of scale.” While the policy of low-price industrial land supply can foster initial clustering, empirical results confirm that this price misallocation enables the survival of low-efficiency enterprises, creating a pattern of “ineffective agglomeration.” This ultimately suppresses TFP and hinders the upgrading of the industrial structure, which is detrimental to the enhancement of long-term economic resilience.

The findings related to land market monopoly by local governments and their significant fiscal reliance on land supply reflect distinct Chinese characteristics. However, the mechanisms through which land misallocation weakens economic resilience by impeding technological progress, and through which it sustains inefficient firms and lowers overall productivity, align with broader international research on factor allocation and hold general relevance across different contexts.

The above research conclusions indicate that land misallocation is a significant bottleneck currently constraining China’s high-quality economic development, as it weakens regional economic resilience by inhibiting technological progress, among other channels. In the future, further market-oriented reforms of production factors are needed to shift land resource allocation from “administrative dominance” to “efficiency priority,” providing institutional guarantees for building a more resilient modern economic system. Accordingly, this research proposes the following policy implications to alleviate land misallocation and enhance regional economic resilience: First, optimize the land supply structure under the New Urbanization context. Implement a “population–land–fund linkage” mechanism, dynamically matching land supply prices with labor mobility directions and industrial demands to avoid inefficient allocation of land resources. Second, accelerate the fiscal transformation of local governments and build a diversified fiscal revenue system. Reduce local governments’ reliance on land conveyance revenue and supplement fiscal revenue through long-term tax categories such as property taxes and resource taxes. Third, strengthen policy coordination between land supply and industrial upgrading. Link land policies with innovation policies, granting land use priority or price preferences to high-technology enterprises to incentivize productivity improvements. For instance, include R&D investment or patent output clauses in land supply agreements, forming a “technology-oriented” land supply incentive mechanism.