Mechanism and Measurement of the Effects of Industrial Agglomeration on Agricultural Economic Resilience

Abstract

This study investigates the potential of agricultural industrial agglomeration to bolster agricultural economic resilience and identifies the underlying pathways. We developed an analytical framework for agricultural economics that integrates the concept of “resilience”. This framework facilitates an examination of the influence of agricultural industrial agglomeration on agricultural economic resilience, focusing on two key aspects: enhancement of income and reduction of costs. Utilizing panel data from 30 provincial-level regions in China covering the period from 2006 to 2021, this research empirically assesses the impact, underlying mechanisms, and regional variations of agricultural industrial agglomeration on agricultural economic resilience. The findings reveal that agricultural industrial agglomeration significantly boosts agricultural economic resilience. This positive influence manifests through two primary channels: firstly, “agricultural industrial agglomeration → enhancement of socialized services → agricultural economic resilience” and secondly, “agricultural industrial agglomeration → improvement of agricultural production efficiency → agricultural economic resilience”. The contribution of agricultural industrial agglomeration to agricultural economic resilience is particularly pronounced in major grain-producing regions, notably enhancing capabilities for reconstruction and reinvention, as well as adjustment and adaptation. The study concludes with recommendations aimed at strengthening agricultural economic resilience. These recommendations emphasize the critical role of agricultural industrial agglomeration in fostering agricultural economic resilience, its contribution to the growth of rural economies and the enhancement of socialized services, and the need to consider regional disparities in the process of developing agricultural economic resilience.

1. Introduction

In recent decades, the world has undergone a significant transformation, unparalleled in the previous century. The global landscape is now marked by intensified geopolitical conflicts, recurrent extreme weather events, a resurgence of trade protectionism, and a growing trend of anti-globalization. These factors have collectively heightened the instability in global economic development. In response to these multifaceted risks and challenges, governments worldwide are striving to develop capabilities to resist, adapt, and recover, aiming to swiftly transition to stable economic growth trajectories following disruptive events. This objective closely aligns with the concept of “resilience”, a term frequently employed to describe key attributes of domestic economic operations. Originally a physics concept used to measure the ability of materials to revert to their original shape after deformation, the term “resilience” has increasingly been applied to economics. In this context, it reflects the capacity of an economic system to withstand and recover from external shocks [1]. Enhancing resilience is not only a crucial strategy for economic recovery but also a key focus in pursuing stable and sustainable economic growth. This shift towards enhancing resilience has become a prominent aspect of high-quality economic development and has garnered considerable scholarly attention. The application of resilience spans various domains, including ecosystem resilience, regional economic resilience, and resilience in industrial development [2].

Agriculture is fundamental to the development of a nation’s economy and the stability of its society. To address these challenges and ensure stable development, enhancing the resilience of the agricultural economy is vital. Resilience in this context refers to the agricultural system’s ability to absorb and adapt to external disturbances through structural adjustments, recover swiftly, and transition to new [3], sustainable growth paths [4]. Bolstering agricultural economic resilience is key not only to fostering the inherent dynamics of agricultural growth and moving from a major agricultural nation to a powerful one but also to ensuring food security and stable economic growth [5]. Therefore, investigating the mechanisms and policy approaches to augment agricultural economic resilience is of significant practical relevance.

While China’s agricultural modernization is progressively advancing, the sector still faces significant challenges in production, distribution, and consumption, remaining vulnerable to risk shocks [6]. The government, acknowledging the unique characteristics of the agricultural industry, has implemented key institutional arrangements and policy tools to cultivate resilience. Strategies include leveraging grain storage in land and technology, utilizing market and resource dynamics, and continuously refining trade methods and patterns for agricultural products. Moreover, the government is diversifying its strategy for importing agricultural products, aiming to establish a robust food security system and a comprehensive global agricultural trade, investment, and market monitoring system. Financial instruments such as futures, insurance, and trusts are also employed to mitigate the effects of international political and trade risks [7]. Agricultural industrial agglomeration, a crucial driver of agricultural modernization, impacts the agricultural system through factor resource reallocation and numerous spillover effects. This agglomeration is anticipated to serve as a novel institutional arrangement to bolster agricultural economic resilience.

The current literature typically approaches agricultural economic resilience metaphorically, focusing on high-quality development [8] and competitiveness enhancement [9]. However, direct studies on agricultural economic resilience are sparse. This concept underscores agriculture’s overall capacity to respond to uncertainty shocks. Diverging from previous research that primarily viewed agricultural economic resilience through resistance and recovery lenses, this paper considers a broader spectrum of resilience, including risk resistance, adjustment, adaptation, and rebuilding capabilities [10]. It establishes a comprehensive evaluation index system to calculate the regional agricultural economic resilience using the entropy weight method. Furthermore, this study expands beyond traditional analyses of industry resilience, which have focused on infrastructure [11], innovation [12], and integration [13]. It includes industrial agglomeration as a factor influencing agricultural economic resilience and seeks to address key questions: Does agricultural industrial agglomeration significantly contribute to resilience enhancement? How can the spillover effects of this agglomeration be more effectively harnessed? What regional differences exist in the resilience governance effect of agricultural industrial agglomeration? Accurate assessment of agricultural economic resilience and understanding the role of agricultural industrial agglomeration are vital for developing policies that foster resilience, ensure food security, and contribute to building an agricultural powerhouse. Based on the above discussion, this paper is structured as follows: literature review, mechanism analysis and research hypotheses, research design, empirical analyses, and conclusions and insights. The next section will focus on verifying whether agricultural industry agglomeration can promote agricultural economic resilience, as well as examine its mechanism of action and heterogeneity.

2. Literature Review

Research pertaining to this article has evolved along three distinct threads, with the first primarily centered on the essence and quantification of economic resilience. The concept of “resilience”, originating in natural sciences like physics and engineering, denotes the ability of objects to revert to their original state after deformation. Holling [14] introduced this notion to ecosystem studies, characterizing it as the capacity of ecosystems to withstand environmental disruptions and recover subsequently. The concept has transitioned from its initial focus on equilibrium-based resilience in physical, engineering, and ecological realms to an evolution-based adaptive resilience [15]. This involves examining how systems adjust dynamically to environmental alterations amid shocks and fluctuations [16]. Economic resilience, building upon the broader framework of resilience, elucidates the varying capacities of economic entities to endure shocks and recover amidst economic cycles and external influences [1]. It highlights the ongoing adaptation and adjustment of economic systems within evolving economic contexts [17], aiming at their recuperation and enhancement [18]. The resilience of regions and industries as complex systems is a higher-order construct that encompasses multiple dimensions. To measure economic resilience, scholars have adopted two primary methods: the index system method and the core variable method. Briguglio initially developed a range of indicators encompassing macroeconomic stability, micro-market efficiency, and the level of social public services to gauge national economic resilience [19]. The literature on economic resilience in China focuses mainly on the macroeconomic and regional economic areas. Subsequent research has constructed multidimensional indicator systems centered on industrial development to assess economic resilience [20], with some studies concentrating on aspects of resistance and recovery [21]. These have employed the core variable method, comparing actual and expected economic outputs to measure economic resilience [22]. Liu Xiaoxing et al. in 2021 quantitatively evaluated macroeconomic resilience from a systemic risk standpoint, considering the intensity and duration of risk absorption [23]. Studies focusing on the agricultural sector’s economic resilience primarily investigate regional agricultural economic resilience levels and their influencing factors [24,25].

The second thread of research relevant to this article centers on the factors influencing economic resilience, encompassing studies on regional economic resilience. These studies are bifurcated into analyses of internal regional economic characteristics and external environments. Literature examining internal characteristics identifies pivotal roles for factors such as industrial structure [26], innovation capacity [27], and total factor productivity [28]. Conversely, studies focusing on the external environment of regional economies highlight the significant positive influence of aspects like government actions [29], the development of the digital industry [30], and strategic coupling [31] in enhancing regional economic resilience. Additional research avenues include industrial resilience and supply chain resilience [32,33]. However, studies specifically addressing agricultural economic resilience, examining elements like rural industry integration [2], agricultural infrastructure [11], and digital economy development [34], remain relatively limited.

The third thread delves into the effects of industrial agglomeration. Firstly, its impact on economic growth is notable. Some scholars have discovered that both single-industry and collaborative industrial agglomerations significantly foster economic growth [35]. From the angles of production efficiency and technological innovation, it has been observed that collaborative agglomeration of manufacturing and productive service industries boosts production efficiency, spurs innovation, and stimulates economic growth [36]. Additionally, industrial agglomeration centered on diversification, particularly considering knowledge spillovers, is deemed more favorable for regional economic growth [37]. Secondly, the impact of industrial clustering on high-quality economic development is broadly acknowledged in academia. Research indicates that agglomeration externalities are crucial in accelerating high-quality urban economic development [38]. Industrial agglomeration contributes to local high-quality economic development through mechanisms like industrial structure adjustment [39], technological innovation [40], and industrial structure upgrading [41]. Lastly, the influence of industrial agglomeration on economic resilience varies. Scholars suggest that the technological spillover effects of industrial agglomeration can enhance urban economic resilience [42], yet the outcomes differ across industries. For instance, tertiary industry financial agglomeration significantly bolsters urban economic resilience, demonstrating dual-threshold and diminishing marginal effects in a nonlinear relationship [43,44]. The secondary industry’s industrial agglomeration has a “U-shaped” relationship with economic resilience, indicating that the current level of specialized industrial agglomeration in China is not optimal for boosting economic resilience [45]. Nonetheless, research on the impact of primary industry agricultural agglomeration on economic resilience is still scarce.

The literature review reveals that as research on resilience deepens, the concept of resilience is increasingly recognized as a crucial topic in the field of economics. While previous studies offer valuable insights, there remain areas ripe for further investigation. Notably, agricultural economic resilience is still in its nascent stages compared to the broader concept of economic resilience. This paper introduces a new perspective by examining the role of industrial agglomeration in enhancing resilience governance, aiming to make marginal contributions in two key areas: (1) quantitatively assessing the agricultural economic resilience of various regions. This approach considers three dimensions: the capacity to prevent supply chain disruptions and regenerate economic activities post-shocks, the ability to adjust and adapt to both natural and market risks, and the capability to reconstruct and innovate new development paths in response to shocks. This involves the systematic creation of an evaluation indicator system for agricultural economic resilience and the measurement of regional resilience; (2) exploring the relationship between agricultural industrial agglomeration and agricultural economic resilience. This examination includes direct, indirect, and heterogeneous impacts, providing insights for further enhancing agricultural economic resilience.

3. Mechanism Analysis and Research Hypotheses

Integrating “resilience” into the analytical framework of agricultural economics broadens the perspective of addressing agricultural development issues, shifting from a focus solely on risk resistance. Drawing on authoritative texts and previous scholarly work (in the Chinese Dictionary, “resilience” is interpreted as “not easy to break despite deformation by external forces, as opposed to ‘brittle’”), this paper posits that agricultural economic resilience encompasses the agricultural system’s intrinsic ability to self-adjust, mitigate the effects of external shocks, rapidly recover, and pivot towards new growth trajectories to sustain expected development objectives. This resilience is characterized by risk resistance, adaptability, and the capacity for reconstruction and reinvention. Furthermore, as illustrated in Figure 1, agricultural economic resilience is defined by several key aspects: first, it represents a dynamic capability rather than a static outcome, differentiating it from agricultural vulnerability; second, agricultural economic resilience is not binary but exists on a spectrum; third, it primarily focuses on the capacity to adjust, transform, and achieve objectives following risk shocks. Given the unpredictability of the timing and magnitude of risk impacts, agricultural economic resilience extends beyond mere risk management. It emphasizes the adjustment ability at the moment of impact and the capacity for post-impact reinvention and transformation.

Agricultural economic resilience is dynamic in nature, signifying that the regional agricultural economic system is in a state of constant flux due to element recombination in response to shocks. This results in the dynamic evolution and strengthening of agricultural economic resilience. Additionally, agricultural economic resilience is characterized by interactions among multiple agents, such as industry linkages, market structures, laborer behavior, and government institutional arrangements and policies. These interactions between the market, labor force, and government collectively shape the resilience of the regional agricultural economy.

3.1. Direct Impact of Agricultural Industrial Agglomeration on Agricultural Economic Resilience

Agricultural production, characterized by high dependence on natural resource inputs and high transaction costs, tends to cluster in favorable regions, leading to the formation of industrial agglomerations [46]. Based on the theory of agglomeration economics, industrial agglomeration impacts regional economic development through various mechanisms, including matching, sharing, and learning, thereby influencing a region’s capacity to resist and recover from crises [47]. With the increasing scale and intensity of agricultural production, agricultural industrial agglomeration has emerged as an essential strategy to address numerous challenges in China’s agriculture [48]. The diversity in products, layered production processes, and multi-dimensional trade generated by agricultural industrial agglomeration reduce agricultural vulnerability and boost competitiveness. This enables farmers, as key actors in agricultural production and management, to withstand shocks and pressures more effectively. Additionally, the agglomeration, division of labor, competition, and collaboration effects within agricultural industrial agglomeration optimize the comparative advantages of agricultural production, enhance overall agricultural production capabilities, and consequently improve agricultural economic resilience. Therefore, the first research hypothesis of this paper is proposed:

H1: 

Agricultural industrial agglomeration contributes to enhancing agricultural economic resilience.

3.2. Indirect Impact of Agricultural Industrial Agglomeration on Agricultural Economic Resilience

The indirect of agricultural industry agglomeration on the resilience of the agricultural economy is shown in Figure 2.

The relationship between agricultural industrial agglomeration (measured by the location entropy index, same below), agricultural socialized services (measured by the logarithm of the value of output of professional and auxiliary activities in agriculture, forestry, and fisheries), and the enhancement of agricultural economic resilience is multifaceted. As an embodiment of the agglomeration economy, agricultural industrial agglomeration plays a crucial role in the agricultural value chain. It acts as a supplier, offering upstream products and services to agricultural producers, thereby reducing their production costs. Enhancements in the manufacturing of agricultural production and processing machinery drive the improvement of agricultural socialized service levels. This facilitates the scaling and specialization of agricultural product production. Furthermore, within the framework of agricultural industrial agglomeration, the integration of agriculture with commerce, trade, and science addresses gaps in the sector, consolidates industry linkages, and strengthens the system’s ability to resist shocks. Agricultural socialized services, through cooperative effects, are instrumental in transforming agricultural production methods. They address the inefficiencies caused by land fragmentation and shift the production model from a family-based division of labor to a societal one [49]. This optimizes labor allocation among agricultural participants [50], enhances risk management capabilities, and, in turn, bolsters agricultural economic resilience. Consequently, the second research hypothesis is proposed:

H2a: 

Agricultural industrial agglomeration boosts the level of agricultural socialized services, thereby improving agricultural economic resilience. This implies that agricultural socialized services serve as a conduit through which agricultural industrial agglomeration strengthens agricultural economic resilience.

Exploring the relationship between agricultural industrial agglomeration, agricultural production efficiency, and the enhancement of agricultural economic resilience reveals that the external effects of agricultural industrial agglomeration are significant. Shared infrastructure, intermediate inputs, and technological spillovers in agricultural production allow participants in agglomerated areas to access labor, products, and technology more efficiently and cost-effectively. This reduces transaction costs, fosters innovation, encourages knowledge spillovers, and achieves specialization and synergy in agricultural production, thereby raising the industry’s added value and enhancing agricultural production efficiency. A robust economic foundation provides greater flexibility for agriculture to adjust during risk shocks [24], and improved efficiency equips the agricultural system better to manage external shocks. The traditionally slow technological progress in agriculture has limited labor productivity, making it challenging for the sector to achieve average, let alone excess, profits [51]. Enhancing agricultural production efficiency not only promotes increased production and income but also improves the flexibility of primary agricultural production. This leads to the formulation of the third research hypothesis:

H2b: 

Agricultural industrial agglomeration advances agricultural production efficiency (quantified by the total output value of agriculture, forestry, animal husbandry, and fishery per unit of labor), thereby enhancing agricultural economic resilience. Thus, improving agricultural production efficiency is a pathway through which agricultural industrial agglomeration empowers agricultural economic resilience.

4. Research Design

4.1. Variable Selection

4.1.1. Dependent Variable

This paper’s dependent variable is agricultural economic resilience. Reflecting on the earlier discussion of its connotation, the construction of the indicator system for measuring agricultural economic resilience draws on the “Pressure-State-Response” (PSR) model. (The PSR model is a dynamic modelling structure for analyzing human–environment interactions and consists of three indicator layers: pressure, state and response. Pressure refers to the destructive impacts of human activities on the environment, state refers to the changes that occur under the pressure of the destructive impacts, and response refers to the policies and measures adopted by governments, social organizations, businesses, households, etc.) The developed indicator system comprises 14 secondary indicators spread across three dimensions: risk resistance ability (P), adjustment and adaptation ability (S), and reconstruction and reinvention ability (R), as detailed in

Table 1. Comprehensive evaluation index system of agricultural economic resilience.

Primary Indicator	Secondary Indicator	Explanation	Indicator Unit
Risk Resistance Ability (P)	Effective Irrigation Area Rate	Effective Irrigation Area/Cropped Area	Ratios
	Agricultural Machinery Power per Unit Area	Total Agricultural Machinery Power/Cropped Area	KW/Hectare
	Agricultural Disaster Resistance Ability	(Disaster-Affected Area—Disaster-Damaged Area)/Disaster-Affected Area	Ratios
	Pure Fertilizer Quantity per Unit Sown Area	Pure Fertilizer Applied/Cropped Area	Tons/Hectare
	Pesticide Usage per Unit Sown Area	Pesticide Usage/Cropped Area	Tons/Hectare
	Agricultural Film Usage per Unit Sown Area	Agricultural Film Usage/Cropped Area	Tons/Hectare
	Area of Soil and Water Conservation	China Rural Statistical Yearbook	Hectare
Adjustment and Adaptation Ability (S)	Land Productivity	Agricultural Output per Hectare	Billions of Yuan/Hectare
	Rural Residents’ Consumption Expenditure Level	Rural Residents’ Consumption Expenditure	Yuan
	Employment Proportion in Agriculture	Agricultural Workers/Total Rural Employment	Ratios
Reconstruction and Reinvention Ability (R)	Investment in Agricultural Fixed Assets	Fixed Asset Investment in Agriculture, Forestry, Animal Husbandry, and Fishery by Rural Households	Billions of Yuan
	Fiscal Support for Agriculture	Fiscal Expenditure on Agriculture	Billions of Yuan
	Rural Economic Status	Value Added of Primary Industry as a Percentage of Regional GDP	Ratios
	Rural Electricity Consumption	Electricity Consumption for Production and Living in Rural Areas	KW h

. Among them, risk resistance ability includes seven indicators, namely, effective irrigated area rate, agricultural machinery power per unit area, agricultural disaster resistance ability, pure fertilizer quantity per unit sown area, pesticide usage per unit sown area, agricultural film usage per unit sown area, and area of soil and water conservation; adjustment and adaptation ability includes three indicators, namely, land productivity, rural residents’ consumption expenditure level, and employment proportion in agriculture; and reconstruction and reinvention ability includes four indicators, namely, investment in agricultural fixed assets, fiscal support for agriculture, rural economic status, and rural electricity consumption.

These dimensions collectively assess regional agricultural economic resilience. The entropy method is then employed to assign weights to the indicators in the agricultural economic resilience system. Using this method, a comprehensive score representing the level of regional agricultural economic resilience is calculated.

4.1.2. Core Explanatory Variable

The core explanatory variable in this study is agricultural industrial agglomeration. Various methods exist to measure industrial agglomeration, including industry concentration, the Herfindahl–Hirschman Index, the location quotient, and the spatial Gini coefficient, each suited for different contexts. Industry concentration is typically used to determine the market agglomeration degree of enterprises. The Herfindahl–Hirschman Index is commonly applied in gauging industry concentration, while the spatial Gini coefficient is often used to assess the distribution of manufacturing industries [52]. Considering data availability and the necessity to account for regional scale differences [53], this study adopts location entropy as the metric for measuring the degree of agricultural industrial agglomeration. The calculation formula for this is as follows:

$$L{Q}_{ij}=(a_{ij}/{gdp}_{ij})/(A_j/{GDP}_j)$$

(1)

$L{Q}_{ij}$ represents the location entropy index of the agricultural industry for province$i$ in year $j$, which signifies the extent of agricultural industrial concentration in province $i$ for year $j$. $a_{ij}$ denotes the agricultural output of province $i$ in year $j$, while ${GDP}_{ij}$ corresponds to the regional GDP of province $i$ for year $j$. $A_j$ refers to the national total agricultural output in year $j$, and ${GDP}_{ij}$ signifies the overall national production value for year $j$.

4.1.3. Other Variables

In addition to the core variables, this paper includes two variables to assess the impact of agricultural industrial agglomeration on agricultural resilience: (1) agricultural production efficiency. There are many variables that can characterize the efficiency of agricultural production, such as total factor productivity in agriculture, agricultural labor productivity, etc. In this paper, we refer to the most basic concept of productivity, i.e., agricultural output per unit of labor to measure the efficiency of agricultural production, which is expressed in terms of the total output value of the agriculture, forestry, animal husbandry, and fishery industries of Laogun, and which is quantified by the total output value of agriculture, forestry, animal husbandry, and fishery per unit of labor; (2) agricultural socialized services. Given the absence of direct measures in existing statistical data, this study follows common practice by using the logarithm of the output value of specialized and auxiliary activities in agriculture, forestry, animal husbandry, and fishery to represent the development level of regional agricultural socialized services. Furthermore, considering that factors such as the level of science and innovation and market potential may also have an impact on the resilience of the agricultural economy, the paper further controls for the following variables: (1) level of science and innovation, indicated by the ratio of fiscal science and technology expenditure to the total general public budget expenditure of local governments; (2) market potential, measured by the logarithm of the population to administrative area ratio for each province [4]; (3) scale of operations, represented by the average cultivated land area per labor in each province; (4) urban economic development level, denoted by the combined output value of the secondary and tertiary industries in the region; (5) market size, reflected by the logarithm of the total retail sales of consumer goods in each province. Descriptive statistics for each variable are shown in

Table 2. Variable definitions and descriptive statistics.

Variable Name	Measurement Method	Sample Size	Mean	Standard Deviation	Minimum Value	Maximum Value
Agricultural Economic Resilience	Comprehensive Indicator System (Table 1) Entropy Value Method Measurement	480	0.170	0.060	0.060	0.370
Agricultural Industry Agglomeration	Location quotient	480	2.010	1.030	0.080	6.020
Science and Innovation Level	Share of fiscal S&T expenditures in local government general public budget expenditures in each province	480	0.020	0.010	0.000	0.070
Market Potential	Ratio of population size to administrative area of each province	480	0.050	0.070	0.000	0.400
Business Scale	Cultivated land area per laborer in each province expressed	480	3.720	0.470	2.260	4.650
Urban Economic Development Level	Sum of regional output value of secondary and tertiary industries	480	5.720	4.450	0.000	33.320
Market Scale	Total retail sales of consumer goods by province in logarithmic terms	480	4.090	0.430	2.720	5.060
Agricultural Socialization Services	Logarithmic value of output of specialized and auxiliary activities in agriculture, forestry, animal husbandry and fishery in each province	480	4.290	1.250	1.100	6.780
Agricultural Production Efficiency	Ratio of agricultural output to agricultural labor force	480	4.790	3.020	0.490	17.37

.

4.2. Data Sources

The research sample for this study comprises panel data from 30 provincial-level administrative regions in China, excluding Tibet, Hong Kong, Macau, and Taiwan due to data availability. The data span a 16-year period from 2006 to 2021. These data are sourced from the China Statistical Yearbook, the China Rural Statistical Yearbook, and various provincial statistical yearbooks. To maintain consistency, economic indicators are adjusted for inflation based on the year 2006. In cases of missing data, interpolation methods are employed to calculate and determine the values.

4.3. Model Setting

Considering the individual characteristic differences in the sample data and having passed the Hausman test, the data used in this study are balanced panel data, and this paper adopts a fixed effects model to test the impact of agricultural industrial agglomeration on agricultural economic resilience. The specific model is as follows:

$${Aer}_{it}={\beta }_0+{\beta }_1{Aia}_{it}+{\beta }_2{Controls}_{it}+{\mu }_i+{\mathsf{\omega }}_t+{\mathsf{\epsilon }}_{it}$$

(2)

In Formula (2), ${Aer}_{it}$ and ${Aia}_{it}$ respectively represent the dependent variable, agricultural economic resilience, and the explanatory variable, the level of agricultural industrial agglomeration, in province $i$. ${Controls}_{it}$ stands for other control variables. ${\mu }_i$ and ${\mathsf{\omega }}_t$ are dummy variables for the province and year, respectively, used to control for individual and time effects. ${\mathsf{\epsilon }}_{it}$ is the random disturbance term.

5. Empirical Analysis

5.1. Trends in Agricultural Industrial Agglomeration and Agricultural Economic Resilience

Employing the entropy method, this study measured the agricultural economic resilience of each region from 2006 to 2021. Concurrently, the level of agricultural industrial agglomeration in each region for the same period was calculated using Formula (1). The evolution and spatiotemporal characteristics of both were then analyzed from spatial and temporal perspectives, as detailed in

Table 3. Trends in agricultural economic resilience (measurements based on the above indicator system), 2006–2021.

Year	Maximum Value	Minimum Value	Mean Value of the Whole Sample	Mean Value of Main Grain-Producing Areas	Mean Value of Non-Food-Producing Areas
2007	0.1971	0.0623	0.1252	0.1389	0.1148
2009	0.3216	0.0711	0.1538	0.1741	0.1383
2011	0.2711	0.0849	0.1478	0.1649	0.1348
2013	0.3117	0.0924	0.1658	0.1806	0.1544
2015	0.3342	0.0994	0.1823	0.1980	0.1704
2017	0.3479	0.1096	0.1956	0.2063	0.1873
2019	0.3681	0.1228	0.2119	0.2202	0.2055
2021	0.2731	0.1194	0.2037	0.2162	0.1942

and

Table 4. Trends in agricultural industrial agglomeration (location entropy), 2006–2021.

Year	Maximum Value	Minimum Value	Mean Value of the Whole Sample	Mean Value of Main Grain-Producing Areas	Mean Value of Non-Food-Producing Areas
2007	4.3745	0.2050	2.0250	2.3145	1.8035
2009	4.4228	0.1953	1.9946	2.2409	1.8063
2011	3.6532	0.1564	1.6706	1.8523	1.5316
2013	3.9321	0.1557	1.8169	2.0443	1.6430
2015	4.1010	0.1376	1.9729	2.2037	1.7964
2017	4.3881	0.1206	2.0251	2.2063	1.8865
2019	5.6452	0.0939	1.9893	2.2313	1.8043
2021	6.0176	0.0785	2.1372	2.4368	1.9081

.

5.1.1. Trends in Agricultural Economic Resilience

Table 3 presents the raw data on agricultural economic resilience from 2006 to 2021. The data indicate a general upward trend in agricultural economic resilience, with the average value rising from 0.1252 to 0.2037. This increase can be attributed to China’s sustained emphasis on the development of agriculture, rural areas, and farmers. The consistent progress of the rural revitalization strategy, the ongoing evolution of modern agriculture tailored to Chinese conditions, and the step-by-step implementation of becoming an agricultural powerhouse have collectively contributed to the significant enhancement of the agricultural economic system’s resilience. An examination of different regions reveals that the agricultural economic resilience is more advanced in major grain-producing areas, while it is less developed in non-major grain-producing regions.

5.1.2. Trends in Agricultural Industrial Agglomeration

Table 4 displays data concerning agricultural industrial agglomeration from 2006 to 2021, revealing an upward trend. This growth suggests advances in the development of agricultural agglomeration economies. However, the current state of agricultural industrial agglomeration remains in a nascent phase, with substantial potential for expansion. A regional analysis indicates a disparity in agglomeration levels between major and non-major grain-producing areas in China. The former, as hubs of agricultural production and operation, exhibit significantly higher agglomeration levels, underscoring the prevailing imbalance in China’s agricultural development.

5.2. Analysis of Baseline Regression Results

To ensure the reliability of regression model estimates and address potential multicollinearity among variables, a collinearity diagnosis was conducted. The diagnostic results revealed a maximum Variance Inflation Factor (VIF) of 2.68, well below the threshold of 10, thereby negating concerns of multicollinearity.

Table 5. Benchmark regression results.

	(1)	(2)	(3)
	Aer	Aer	Aer
Aia	0.0178 ***	0.00893 **	0.0146 ***
	(0.00241)	(0.00392)	(0.00400)
Science and Innovation Level	0.428 **		0.432
	(0.173)		(0.457)
Market Potential	0.0748 *		0.874 ***
	(0.0393)		(0.142)
Business Scale	−0.0269		0.0584 **
	(0.0243)		(0.0268)
Level of urban economic development	0.00127 ***		−0.0000960
	(0.000302)		(0.00165)
Rural human capital level	0.131 ***		−0.0282
	(0.0287)		(0.0792)
Constant	−0.319 ***	0.0946 ***	−0.0420
	(0.0347)	(0.00827)	(0.259)
Province Fixed Effects	NO	YES	YES
Year fixed effects	NO	YES	YES
N	480	480	480
R2	0.620	0.638	0.665

Note: *, **, and *** denote significance at the 10%, 5%, and 1% statistical levels, respectively; robust standard errors are in parentheses.

examines the influence of agricultural industrial agglomeration on agricultural economic resilience. The Ordinary Least Squares (OLS) regression results are detailed across three columns: Column (1) presents results without provincial and annual fixed effects; Column (2) includes these effects but omits control variables; Column (3) incorporates both effects and control variables. In all scenarios, the core variable—agricultural industrial agglomeration—displays a positive and significant correlation at a minimum of the 5% level with agricultural economic resilience. This finding corroborates Hypothesis 1, asserting that higher levels of agglomeration bolster agricultural economic resilience. The diversification of products, layered production structures, and multifaceted trade resulting from agglomeration diminish agricultural vulnerability and boost competitiveness. These factors enable agricultural producers and managers to withstand external shocks and pressures more effectively. Additionally, the agglomeration, division of labor, competition, and collaboration effects within agricultural industrial agglomeration optimize the comparative advantages of agricultural production, enhancing overall capabilities and thus agricultural economic resilience.

In terms of control variables (Column (3)), the market potential’s impact coefficient is significantly positive at the 1% level, suggesting that market potential notably fosters agricultural economic resilience. This may be attributed to the broader demand spectrum for agricultural products, which enhances the sector’s economic efficiency and resilience. Similarly, the positive impact coefficient of operational scale at the 5% level indicates that economies of scale from expanded operations contribute to the fortification of agricultural economic resilience.

5.3. Robustness Test

To assess the robustness of the previously mentioned regression results, four distinct methods were employed.

5.3.1. Exclusion of Municipality Samples

Acknowledging the unique characteristics of agricultural production and operation in municipalities, the regression excluded data from Beijing, Tianjin, Shanghai, and Chongqing. Utilizing the remaining 416 samples, Equation (2) was re-estimated. As

Table 6. Robustness tests.

	(1)	(2)	(3)	(4)
	Municipality Sample Excluded	Replacement Model (Tobit)	Lagged One Period Core Explanatory Variables	Instrumental Variable 2sls
	Aer	Aer	Aer	Aer
Aia	0.0174 ***	0.0212 ***
	(0.00443)	(0.00367)
Lag Aia		0.486 **	0.0222 ***	0.027 ***
		(0.200)	(0.00579)	(0.006)
Science and Innovation Level	4.219 ***	0.195 **	0.784	0.579 **
	(0.662)	(0.0823)	(0.542)	(0.282)
Market Potential	0.0319	0.0567 **	1.265 ***	1.421 ***
	(0.0225)	(0.0228)	(0.178)	(0.271)
Market Scale	0.00232 **	0.000794	0.0374	0.040 **
	(0.000985)	(0.000686)	(0.0276)	(0.020)
Business Scale	0.0800 **	0.0516 *	−0.000577	0.000
	(0.0351)	(0.0274)	(0.00198)	(0.001)
City Economic Development Level	−0.452 ***	−0.317 ***	−0.00874	0.058 **
	(0.110)	(0.0363)	(−0.10)	(0.029)
Constant	0.0174 ***	0.0212 ***	−0.0766	0.027 ***
	(0.00443)	(0.00367)	(0.259)	(0.006)
Kleibergen–Paap rk LM				45.026 ***
Cragg–Donald Wald F				228.216
Province Fixed Effects	YES		YES	YES
Year Fixed effects	YES		YES	YES
N	416	480	450	450
R2	0.824		0.625	0.564

Note: *, **, and *** denote significance at the 10%, 5%, and 1% statistical levels, respectively; robust standard errors are in parentheses.

, Column (1) illustrates, the coefficient of agricultural industrial agglomeration remains significantly positive at the 1% level, affirming the validity of the initial conclusion.

5.3.2. Modification of the Baseline Model

Given that the measure of agricultural economic resilience in this study ranges from 0 to 1, fitting the criteria for a limited dependent variable model, the panel Tobit model was applied to estimate Equation (2). Table 6, Column (2) displays results that align with the baseline regression in terms of direction and significance at the 1% level, confirming consistency in the findings upon altering the model.

5.3.3. Lagging the Explanatory Variable

To account for the potential delayed effects of agricultural industrial agglomeration, Equation (2) was re-estimated with the agglomeration variable lagged by one period. The results, detailed in Table 6, Column (3), show alignment in direction and a 1% level significance with the baseline regression. This suggests a “snowball effect” in agricultural industrial agglomeration, where its current level contributes to subsequent agricultural economic resilience.

5.3.4. Instrumental Variable Method

To address possible reverse causality between agricultural industrial agglomeration and agricultural economic resilience—where enhanced resilience might intensify agricultural agglomeration—the lagged one-period agricultural industrial agglomeration was utilized as an instrumental variable for Two-Stage Least Squares (2SLS) estimation. The results, presented in Table 6, Column (4), confirm the precise identification of the instrumental variable, negating the need for an over-identification test. The Kleibergen–Paap rk LM statistic reveals no under-identification issues, and the Cragg–Donald F statistic surpasses the critical value at the 5% level, refuting the hypothesis of a weak instrumental variable. This validates the appropriateness of the chosen instrumental variable, thereby supporting the robustness of the research conclusion.

5.4. Test of Impact Mechanism

Enhancing agricultural industrial agglomeration significantly contributes to the development of agricultural economic resilience. This process is facilitated through two primary channels: economic growth and the improvement of agricultural socialized services. The present study, drawing on the work of Hongying Yin (2022) [54], empirically examines these two impact mechanisms to validate the role of agricultural industrial agglomeration in strengthening agricultural economic resilience.

Table 7. Results of the mechanism of action test.

	(1)	(2)	(3)	(4)	(5)	(6)
	Agricultural Socialization Services	Aer (Low Level)	Aer (High Level)	Agricultural Production Efficiency	Aer (Low Level)	Aer (High Level)
Aia	0.1340 **	0.0456	0.0205 ***	0.5215 ***	−0.0568	0.0204 ***
	(0.0529)	(0.0304)	(0.0051)	(0.1898)	(0.0313)	(0.0048)
Science and Innovation Level	−0.1320	−0.8390	0.0617	38.9855 ***	−0.9480 ***	0.7720
	(4.3702)	(0.5255)	(0.2601)	(10.6565)	(0.1352)	(0.4738)
Market Potential	−11.6341 ***	1.4181 ***	3.7006 ***	34.4236 ***	1.1138 **	1.2270
	(3.6353)	(0.3474)	(0.5854)	(12.0871)	(0.3309)	(1.2474)
Market Scale	1.0833 ***	0.0503	0.0124	0.6973	0.3245	0.0200
	(0.3307)	(0.0674)	(0.0257)	(1.1413)	(0.1973)	(0.0261)
Business Scale	0.0057	−0.0057	0.0027 ***	0.0732 **	−0.0026	0.0004
	(0.0111)	(0.0036)	(0.0009)	(0.0355)	(0.0031)	(0.0015)
City Economic Development Level	0.0393	−0.1432	0.1323 ***	1.2212	−0.2311	0.0490
	(0.4913)	(0.1369)	(0.0425)	(1.0702)	(0.1247)	(0.0534)
Constant	−1.5942	0.3080	−0.6032 ***	−9.3977 **	−0.1182	−0.2305
	(1.3091)	(0.3923)	(0.1254)	(4.5671)	(0.4730)	(0.1982)
Province Fixed Effects	YES	YES	YES	YES	YES	YES
Year Fixed effects	YES	YES	YES	YES	YES	YES
N	480	120	360	480	59	421
R2	0.971	0.497	0.811	0.906	0.835	0.715

Note: **, and *** denote significance at the 5%, and 1% statistical levels, respectively; robust standard errors are in parentheses.

, in its Columns (1), (2), and (3), focuses on the impact of agricultural socialized services. Regression analysis in Column (1) reveals that agricultural industrial agglomeration substantially boosts the level of agricultural socialized services, thus supporting the study’s initial hypothesis. To delve deeper, the study categorizes the samples into groups with higher and lower levels of agricultural socialized services, using the 25th percentile as a dividing point. This segmentation aims to assess the effect of agricultural industrial agglomeration on agricultural economic resilience under varied conditions, as demonstrated in Columns (2) and (3). The findings show a significant influence of agricultural industrial agglomeration on the high-level group of agricultural socialized services at the 1% level, whereas its impact on the low-level group is not statistically significant. These results affirm the hypothesis that agricultural industrial agglomeration fosters agricultural economic resilience, primarily through enhancing agricultural socialized services.

The study further explores the impact of agricultural production efficiency in Table 7, represented in Columns (4), (5), and (6). Regression analyses indicate a notable enhancement in agricultural production efficiency due to agricultural industrial agglomeration. Similar to the previous analysis, the samples were divided into higher and lower agricultural production efficiency groups based on the 25th percentile. The outcomes, as shown in Columns (5) and (6), reveal a significant impact of agricultural industrial agglomeration on the high-level group of agricultural production efficiency at the 1% level, while its effect on the low-level group remains insignificant. These observations corroborate the theory that agricultural industrial agglomeration bolsters agricultural economic resilience by improving agricultural production efficiency.

Thus, Hypotheses 2a and 2b of this paper are both verified.

5.5. Heterogeneity Analysis

1. Analysis of regional heterogeneity: China’s provinces exhibit considerable diversity in foundational conditions and resource endowments, leading to marked differences in agricultural industrial agglomeration and agricultural economic resilience. These disparities have been shaped by years of regional relationship adjustments, urban–rural dynamics, and human–land interactions, particularly affecting grain-producing areas. To investigate the regional impact of agricultural industrial agglomeration on agricultural economic resilience, the study conducts separate regression estimations for major grain-producing and non-major grain-producing areas. Results presented in

Table 8. Results of subregional regressions.

	(1) Grain Producing Regions	(2) Non-Food Producing Regions
Aia	0.0261 ***	0.0201
	(0.00587)	(0.0129)
Science and Innovation Level	0.555	0.0643
	(0.999)	(0.423)
Market Potential	4.441 *	0.879 ***
	(2.161)	(0.187)
Market Scale	−0.0244	0.0819 *
	(0.0653)	(0.0407)
Business Scale	0.00325 **	−0.00452
	(0.00111)	(0.00263)
City Economic Development Level	0.171 ***	−0.140
	(0.0521)	(0.0125)
Constant	−0.691 **	−0.4223 **
	(0.235)	(0.1502)
Province Fixed Effects	YES	YES
Year fixed effects	YES	YES
N	208	272
R2	0.790	0.649

Note: *, **, and *** denote significance at the 10%, 5%, and 1% statistical levels, respectively; robust standard errors are in parentheses.

, Columns (1) and (2), reveal a significantly positive coefficient for agricultural industrial agglomeration in major grain-producing areas. Conversely, in non-major grain-producing areas, the impact coefficient of agricultural industrial agglomeration appears insignificant. This discrepancy could stem from strategic development differences, where major grain-producing areas, with more favorable agricultural production conditions, are better positioned for agricultural industrial agglomeration to effectively enhance agricultural economic resilience.

2. Analysis of dimensional heterogeneity: Agricultural economic resilience encompasses three dimensions: risk resistance ability, adjustment and adaptation ability, and reconstruction and reinvention ability. The influence of agricultural industrial agglomeration varies across these dimensions. The study further examines this impact using dimension-wise regression estimation, with findings detailed in

Table 9. Results of the sub-dimensional regression.

Full Sample				Grain-Producing Regions			Non-Food Producing Areas
	(1)	(2)	(3)	(1)	(2)	(3)	(1)	(2)	(3)
	Risk Resilience	Adaptive Capacity	Reconstructive Capacity	Risk Resilience	Adaptive Capacity	Reconstructive Capacity	Risk Resilience	Adaptive Capacity	Reconstructive Capacity
Aia	0.00217	0.00510 **	0.00731 ***	0.00424 *	0.00980 ***	0.0120 ***	0.0108	−0.00220	0.0115 ***
	(0.00273)	(0.00188)	(0.00247)	(0.00217)	(0.00126)	(0.00316)	(0.0100)	(0.00378)	(0.00364)
Science and Innovation Level	0.114	0.250 *	0.0684	−0.146	−0.115	0.816	−0.0250	0.414 ***	−0.325
	(0.258)	(0.123)	(0.285)	(0.149)	(0.124)	(0.840)	(0.110)	(0.117)	(0.343)
Market Potential	0.126	0.173	0.575 ***	−0.514	2.160 **	2.795 **	0.170	0.0400	0.668 ***
	(0.115)	(0.139)	(0.206)	(0.584)	(0.776)	(1.136)	(0.192)	(0.118)	(0.167)
Market Scale	0.0212	0.0124	0.0249	−0.0110	0.0119	−0.0253	0.0350	0.0202	0.0267
	(0.0178)	(0.0112)	(0.0167)	(0.00822)	(0.00926)	(0.0539)	(0.0277)	(0.0187)	(0.0276)
Business Scale	−0.000745	−0.000536	0.00119 *	0.000920	0.000222	0.00210 **	−0.00243	−0.00174 *	−0.000352
	(0.00112)	(0.000468)	(0.000687)	(0.000540)	(0.000294)	(0.000796)	(0.00164)	(0.000951)	(0.00143)
City Economic Development Level	−0.0371	0.0152	−0.00635	0.0710 ***	0.0521 ***	0.0479	−0.107	0.0123	−0.0446
	(0.0664)	(0.0131)	(0.0239)	(0.00926)	(0.0121)	(0.0383)	(0.0931)	(0.0202)	(0.0303)
Constant	0.113	−0.0945 **	−0.0607	−0.185 ***	−0.320 ***	−0.186	0.310	−0.0803	0.0618
	(0.212)	(0.0366)	(0.0697)	(0.0482)	(0.0476)	(0.188)	(0.297)	(0.0509)	(0.0845)
Province Fixed Effects	YES	YES	YES	YES	YES	YES	YES	YES	YES
Year Fixed Effects	YES	YES	YES	YES	YES	YES	YES	YES	YES
N	480	480	480	208	208	208	272	272	272
R2	0.00217	0.00510 **	0.00731 ***	0.562	0.959	0.493	0.132	0.832	0.503

Note: *, **, and *** denote significance at the 10%, 5%, and 1% statistical levels, respectively; robust standard errors are in parentheses.

. Analysis of the full-sample dimension-wise regression indicates that agricultural industrial agglomeration significantly boosts adjustment and adaptation ability, as well as reconstruction and reinvention ability. However, its effect on risk resistance ability is not significant. Among these, reconstruction and reinvention ability is most affected by agricultural industrial agglomeration, followed by adjustment and adaptation ability. Comparing the results for major grain-producing and non-major grain-producing areas, it is noted that in major grain-producing areas, agricultural industrial agglomeration predominantly enhances all three resilience abilities, thereby reinforcing agricultural economic resilience. In contrast, in non-major grain-producing areas, agricultural industrial agglomeration primarily enhances the dimension of reconstruction and reinvention ability, contributing to the region’s agricultural economic resilience.

5.6. Discussion and Prospects

With the help of panel data of China’s provincial-level administrative districts from 2006 to 2021, the mechanism and effect of agricultural industry agglomeration empowering agricultural economic resilience were examined. The results show that agricultural industrial agglomeration contributes to the resilience of the agricultural economy within the study interval, which is consistent with the findings of existing studies, with the difference that this study also concludes that agricultural production efficiency and the level of socialized services are two important mediators of agricultural industrial agglomeration that contribute to the resilience of the agricultural economy, which is a complementary finding to the existing studies.

6. Conclusions and Implications

This study delves into the intrinsic mechanisms through which agricultural industrial agglomeration impacts agricultural economic resilience, substantiated by statistical data analysis. Theoretical considerations suggest that agricultural economic resilience effectively encapsulates the agricultural economic system’s capacity for self-adjustment in the face of external shocks, rapid recovery, and the pursuit of new growth trajectories to sustain desired developmental objectives. Agricultural industrial agglomeration bolsters this resilience not only through its inherent effects—including agglomeration, division of labor, competition, and collaboration—but also indirectly by fostering rural economic growth and enhancing the level of agricultural socialized services. Empirical analysis employs panel data from China’s provincial-level administrative regions spanning 2006 to 2021. This investigation yields several key findings: (1) national agricultural economic resilience demonstrated a strengthening trend during the study period, with resilience being notably higher in major grain-producing areas compared to non-major grain-producing regions; (2) agricultural industrial agglomeration exerts a significant positive influence on agricultural economic resilience, suggesting that it plays a crucial role in reinforcing this resilience, a conclusion further affirmed by robustness tests; (3) agricultural production efficiency and the level of socialized services emerge as vital intermediaries in the empowerment of agricultural economic resilience by agricultural industrial agglomeration. This process occurs via two pathways: “agricultural industrial agglomeration → agricultural socialized services → agricultural economic resilience” and “agricultural industrial agglomeration → agricultural production efficiency → agricultural economic resilience”; (4) regionally, the impact of agricultural industrial agglomeration on fostering agricultural economic resilience is more evident in major grain-producing areas. In terms of resilience dimensions, the reconstruction and reinvention ability is most responsive to agricultural industrial agglomeration, followed by adjustment and adaptation ability; (5) in major grain-producing regions, agricultural industrial agglomeration enhances agricultural economic resilience across risk resistance, adjustment and adaptation, and reconstruction and reinvention abilities. Conversely, in non-major grain-producing areas, its influence is primarily observed in the reconstruction and reinvention dimension.

However, due to data availability, this study mainly focuses on the resilience of the regional agricultural economy at the macro level, and does not conduct an in-depth study on the resilience of individual farmers or enterprises at the micro level, which is a direction that should be focused on in the next study.

Drawing from the conclusions of this research, the paper offers several policy recommendations:

Firstly, there is a need to underscore the significance of agricultural industrial agglomeration in fostering agricultural economic resilience. During the development of agricultural industrial agglomeration, sustained focus on tailored institutional arrangements and policy support is imperative. Enhancing the systems and mechanisms that facilitate the consolidated development of the agricultural industry is crucial, as this will amplify the agricultural economic system’s ability to withstand uncertainties.

Secondly, acknowledging the vital role of agricultural industrial agglomeration in stimulating rural economic growth and improving the level of socialized services is essential. This recognition necessitates further refinement of the agricultural industry’s layout, system, and value chain. Innovation must remain a key focus in agricultural economic development to establish a new equilibrium of supply and demand at a more advanced level, thereby strengthening the system’s resilience and recovery capabilities in the face of unforeseen events.

Thirdly, it is important to recognize and address regional variability in developing agricultural economic resilience through agricultural industrial agglomeration. Different regions should adapt their approaches to building agricultural resilience based on their unique advantages and local conditions. Effectively utilizing agricultural industrial agglomeration in this context is vital. Equally important is ensuring the coordinated development of various aspects within the agricultural economic resilience system, particularly by addressing its weaknesses to foster a holistic enhancement of resilience. Such a comprehensive approach will better prepare the agricultural economic system to manage complex risks emerging from natural, economic, and social systems, as well as their interconnected dynamics.