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 Agricultural Science; Vol. 7, No. 1; 2025 
ISSN 2690-5396   E-ISSN 2690-4799 

https://doi.org/10.30560/as.v7n1p36 

36                             Published by IDEAS SPREAD 
 

The Impact of Modern Agricultural Parks on Regional Agricultural 
Economic Growth: A Case Study of Modern Agricultural Parks in 

Southwest China 

Ke Rujuan1 & Chen Jiusheng1 

1 School of Economics and Management, Chongqing Normal University, Chongqing, China 

Correspondence: Ke Rujuan, School of Economics and Management, Chongqing Normal University, Chongqing, 
China. Tel: 86-1-573-623-7348. E-mail:  

 

Received: February 24, 2025      Accepted: March 19, 2025      Online Published: March 21, 2025  

 

Abstract 

The level of agricultural economic development is a critical indicator for assessing the achievements of rural 
revitalization and the construction of a modern agricultural power. How to elevate agricultural economic standards 
remains a significant challenge for China today. This study treats modern agricultural parks as a quasi-natural 
experiment and employs a multi-period difference-in-differences (DID) model to analyze panel data from 98 
modern agricultural parks in southwest China, aiming to explore whether the construction of modern agricultural 
parks can influence regional agricultural economic growth. The findings demonstrate that the establishment of 
modern agricultural parks significantly enhances the level of agricultural economic development. This effect 
remains robust after conducting parallel trend tests, placebo tests, and propensity score matching (PSM)-DID 
checks. Further mechanism analysis reveals that such parks drive agricultural economic growth through 
innovations in agricultural technology. Additionally, heterogeneity analysis indicates that the policy effect of 
modern agricultural parks varies across regions, with stronger impacts observed in areas with moderate 
development levels compared to less developed regions. These research findings provide valuable insights for 
accelerating the construction of a socialist modern agricultural power in the new era and offer a theoretical 
foundation for advancing the modernization of agriculture and rural areas. 

Keywords: national agricultural science and technology parks, agricultural economic development, innovation in 
agricultural technology, southwest region 

1. Introduction 

The level of agricultural economic development is a crucial metric for evaluating the progress of rural revitalization 
and the construction of a modern agricultural power. At present, China is in a critical phase of comprehensively 
advancing the rural revitalization strategy, making the effective enhancement of agricultural economic standards 
an urgent issue that requires immediate attention. The 2024 Central Document No. 1 explicitly states that 
advancing Chinese-style modernization necessitates persistent efforts to strengthen the agricultural foundation and 
promote comprehensive rural revitalization. Against this backdrop, modern agricultural parks, as vital carriers for 
driving agricultural modernization, have garnered significant attention. 

Modern agricultural parks serve not only as key platforms for the dissemination and innovation of agricultural 
technologies (Jiang and Cui, 2009) but also as primary vehicles for promoting the integration of rural industries 
(Luo et al., 2020). The issuance of the "Opinions of the Central Committee of the Communist Party of China and 
the State Council on Implementing the Employment-Priority Strategy and Promoting High-Quality and Full 
Employment" on September 25, 2024, further emphasizes the strategic deployment of guiding capital, technology, 
and labor-intensive industries to shift towards central and western regions. This presents new opportunities for the 
development of modern agriculture in the southwest region. As a pivotal strategic point in the western development 
initiative, the southwest region, with its core cities of Chengdu and Chongqing, plays a significant role in regional 
economic development due to its technological innovation capabilities and its role in ensuring food security. By 
December 2024, the southwest region had established a total of 98 modern agricultural parks, significantly 
contributing to regional grain production and efficiency. Taking Sichuan Province as an example, grain production 
in 2023 increased by 16.7 billion pounds compared to the previous year, setting a new historical record. This 
underscores the strategic value of modern agricultural parks in "hiding grain in the land and in technology." 



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Existing literature on modern agricultural parks primarily focuses on their development status (Lan Ge et al., 2011; 
Yan Ru Li et al., 2009), innovative capabilities (Sun and Chen, 2020; Chang and Luo, 2019; Qian and Wang, 
2021), and spatial regional disparity measurements (Huo et al., 2022), with most studies remaining at a theoretical 
level. While Xue and Zhu (2022) analyzed the impact of modern agricultural parks on regional agricultural 
economic growth based on data from 254 prefecture-level cities in China from 2010 to 2019, research specifically 
targeting the southwest region, with its unique geographical characteristics, remains insufficient. The southwest 
region is characterized by complex terrain, a high proportion of mountainous and hilly areas, low standards of 
high-quality farmland, and issues such as inadequate mechanization and restricted development (Yue, 2018). 
These factors severely constrain the large-scale and industrialized development of regional agricultural economies 
(Liu, 2021; Zeng and Liu, 2018). 

In light of this, this study takes 438 counties in the southwest region as its research subjects, utilizing data from 
the China County Statistical Yearbook and regional statistical yearbooks spanning 2007 to 2021. It employs a 
multi-period difference-in-differences (DID) method to thoroughly investigate the impact of modern agricultural 
park establishment on regional agricultural economic development and its underlying mechanisms. The innovative 
aspects of this research are twofold: first, it focuses on the agricultural economic development at the county level 
in the southwest region, systematically analyzing the effects of modern agricultural park establishment on regional 
economies, thereby offering a new research perspective for the modernization of agriculture in the southwest. 
Second, by exploring the heterogeneity of modern agricultural parks in the southwest region, it enriches the micro-
level research on how modern agricultural parks influence agricultural economic growth, providing significant 
practical implications for enhancing agricultural economic levels and increasing grain production in the southwest 
region. 

2. Theoretical Analysis and Research Hypotheses 

2.1 The Impact of Modern Agricultural Park Development on Regional Agricultural Economic Growth 

In the 1950s, economist François Perrou proposed the non-equilibrium theory of regional economic development, 
known as the "core-periphery theory." This theory posits that growth poles, or focal points of economic growth, 
play a driving role within a system. According to Perrou, growth poles can be categorized into spontaneously 
formed poles and planned and cultivated poles (Wang, 2011). Modern agricultural parks clearly represent planned 
and cultivated growth poles. By establishing these parks, resources such as funds, technology, and talent are 
invested to increase the population density in surrounding areas, thereby improving residents' living standards and 
promoting regional economic growth. During the initial stages of park development, local governments typically 
provide strong financial support and infrastructure development, laying a solid foundation for the park's growth. 
As modern agricultural parks mature and progress, the agricultural economic strength of the region gradually 
increases. Simultaneously, the driving forces of growth poles and the interactive effects of industrial chains become 
more pronounced, effectively promoting sustained regional prosperity and development. In essence, the gradual 
refinement of the parks and the improvement in agricultural economic levels create a virtuous cycle, providing 
strong momentum for the comprehensive revitalization of the regional economy. 

Based on this theory, the following hypothesis is proposed:  

H1: The establishment of modern agricultural parks promotes regional agricultural economic growth. 

2.2 Mechanisms through Which Modern Agricultural Park Development Affects Regional Agricultural Economic 
Growth 

2.2.1 Driving Technological Innovation 

The introduction of agricultural new productive forces, which emphasize innovation-driven development and 
technology empowerment, aims to advance the process of agricultural modernization with Chinese characteristics, 
achieving a historic transformation from a "large country with weak agriculture" to a "large country with strong 
agriculture" (Luo and Geng, 2024). Modern agricultural parks play a critical role in fostering technological 
innovation and the commercialization of research findings. They focus on developing innovative crop varieties 
and production techniques, while also introducing advanced agricultural technologies and production models, 
thereby significantly enhancing the intensity of technological innovation. This comprehensive strategy ensures 
that cutting-edge technologies are integrated into every aspect of agricultural production. The introduction of new 
crop varieties and technologies not only boosts the market competitiveness of agricultural products and increases 
their value-added potential but also drives the innovation of agricultural production methods, leading to significant 
improvements in production efficiency. Additionally, these innovative measures facilitate the optimization of 



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agricultural structures, providing a robust technological foundation for the sustainable development of the 
agricultural industry. 

Based on this theory, the following hypothesis is proposed: 

H2: Modern agricultural parks stimulate agricultural economic growth through technological innovation. 

2.2.2 Promoting Agricultural Industrial Integration   

The concept of rural industrial integration is rooted in the foundational role of agriculture, leveraging new 
agricultural business entities to drive the extension of agricultural value chains, the enhancement of industrial 
functions, the aggregation of production, the integration of resources, and the innovation of organizational 
management systems. This integrated development model seeks to transcend the boundaries of traditional 
agriculture, fostering the deep integration of rural primary, secondary, and tertiary industries. This promotes a 
mutually reinforcing and synergistic development cycle, ultimately leading to the comprehensive revitalization of 
the rural economy and the continuous advancement of agricultural modernization (Sun et al., 2024). As a new type 
of agricultural business entity, modern agricultural parks facilitate rural industrial integration through various 
means, such as extending agricultural value chains, developing multifunctional agricultural applications, and new 
agricultural business models.  

From a micro economic perspective, the rural industrial integration strategy enhances agricultural production 
efficiency by adopting advanced agricultural technologies and optimizing resource allocation. This not only 
increases farmers' economic benefits but also drives overall agricultural economic growth. From a macroeconomic 
perspective, rural industrial integration accelerates the pace of agricultural modernization, promotes rural 
economic diversification, fosters the formation of industrial clusters, and strengthens regional economic 
competitiveness, ultimately contributing to sustained agricultural economic growth.  

Additionally, due to variations in the hierarchical status and strategic positioning of different cities, modern 
agricultural parks possess distinct resource conditions and development goals. These differences result in varying 
impacts of modern agricultural parks on the agricultural economic development of their respective counties. To 
explore how these differences affect the specific effects of modern agricultural parks on regional agricultural 
economies, we propose the following hypotheses: 

H3: Modern agricultural parks drive agricultural economic growth through the promotion of rural industrial 
integration. 

H4: The impact of modern agricultural parks on agricultural economic growth exhibits heterogeneity across 
regions. 

The mechanism through which modern agricultural park development influences agricultural economic 
growth is illustrated in Figure 1. 

Figure 1. The Logical Framework of How Modern Agricultural Park Construction Influences Agricultural 
Economic Development 

 

3. Econometric Model, Variables, and Data Sources 

3.1 Econometric Model 

Different counties were approved to establish modern agricultural parks at different times. Therefore, this study 
employs a multi-period difference-in-differences (DID) approach, treating the establishment of modern 
agricultural parks as a quasi-natural experiment. This method allows us to assess the impact of modern agricultural 
park development on agricultural economic growth by comparing economic data before and after their 
establishment, while also contrasting with a control group (regions without modern agricultural parks). This 



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approach helps to identify the direct effects of modern agricultural parks on agricultural economic growth, 
providing more precise and scientific analysis results. The specific model is as follows: 

ln Y , α β did , γ X , μ λ ϵ ,                         (1) 

i represents the county, and t represents the year. 

ln Y ,  is the dependent variable, representing the level of agricultural economic development i in county in year 
t. 

did ,  is the core explanatory variable, a dummy variable indicating whether county i  established a modern 
agricultural park in year t.It is set to 1 if the park was established (treatment group) and 0 otherwise (control 
group). 

β  is the key coefficient of interest, capturing the effect of modern agricultural park establishment on agricultural 
economic growth. 

X ,  represents control variables that account for factors such as agricultural labor, agricultural machinery, 
agricultural land inputs, etc.  

γ  is the coefficient vector for these control variables. 

μ   and λ  are county and year fixed effects, respectively. 

ϵ ,   is the random error term. 

The study focuses on determining whether β  is statistically significant. If positive and significant, it indicates 
that modern agricultural parks have a positive effect on agricultural economic growth. 

To address selection bias and ensure the validity of the multi-period DID model, this study conducts a parallel 
trend test between the treatment and control groups. This test evaluates whether the trends of the dependent 
variable (agricultural economic development) in the treatment and control groups were parallel before the 
treatment (i.e., the establishment of modern agricultural parks). The model for the parallel trend test is as follows: 

ln Y , α ∑ β did , ∑ β did , γ X , μ λ ϵ ,               (2) 

did ,   and did ,  are sets of dummy variables indicating years before 8 and after 6 the establishment of modern 
agricultural parks. 

β captures the trend in agricultural economic development before the establishment of modern agricultural 
parks.  If β  is not significant, it satisfies the parallel trend assumption. 

β  captures the effect of modern agricultural parks on agricultural economic growth after their establishment. 
To explore the pathways through which modern agricultural parks affect regional agricultural economic growth, 
this study constructs a mechanism test model. The specific models are as follows: 

 tecℎ , α β did , γ X , μ λ ϵ ,                                (3) 

ln Y , α β did , β tecℎ , γ X , μ λ ϵ ,              (4) 

merge , α β did , γ X , μ λ ϵ ,                              (5) 

ln Y , α β did , β merge , γ X , μ λ ϵ ,           (6) 

tecℎ ,  represents the level of technological innovation. 

merge , represents the level of rural industrial integration. 

α 、α 、α 、α represent constant terms. 

β 、β 、β 、β 、β 、β 、γ 、γ 、γ 、γ are coefficients to be estimated. 

The remaining symbols carry the same meanings as defined in the preceding equations. 

3.2 Variable Selection 

3.2.1 Dependent Variable 

The dependent variable represents the indicator used to measure regional agricultural economic level. In previous 
studies, some scholars have utilized the added value of agriculture, forestry, animal husbandry, and fishery or rural 
residents' per capita net income as (Yuan et al., 2021; Huang et al., 2021; Deng and Wang, 2020). To mitigate the 
impact of price fluctuations on research results, this study follows the methodologies of Wang et al. (2021), Huang 
et al. (2021), and Deng et al. (2020) by employing the annual total output value of agriculture, forestry, animal 



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husbandry, and fishery at the county level as a proxy variable. Data are log-transformed to more accurately reflect 
the actual regional agricultural economic levels. 

3.2.2 Core Explanatory Variable 

The core explanatory variable is a binary variable indicating whether a modern agricultural park has been 
established. Based on the earliest year each county was approved to establish a modern agricultural park, the 
variable is assigned a value of 1 if the county has a modern agricultural park in the given year or any subsequent 
year, and 0 otherwise. This binary variable captures the treatment effect of modern agricultural park establishment, 
contrasting the outcomes of counties with such parks to those without. 

3.2.3 Control Variables 

This study incorporates six control variables to account for factors that may influence regional agricultural 
economic growth, drawing from the methodologies of Xue et al. (2023) and Yin (2020). These variables are: 

Agricultural Labor: The number of persons employed in agriculture, forestry, animal husbandry, and fishery 
reflects labor input in agricultural development. Notably, the population engaged in these sectors has declined 
annually as rural residents migrate to urban areas. 

Agricultural Machinery: The total power of agricultural machinery used in a region indicates the level of 
agricultural mechanization, enhancing production efficiency and crop output. 

Agricultural Land Inputs: The total cropped area serves as an indicator of a region's agricultural land resources. 

Farmers' Income Levels: This encompasses income from farming, animal husbandry, and 务工  activities, 
assessing the economic well-being of farmers. 

Agricultural Output Levels: This measures regional agricultural production capacity and directly impacts 
agricultural economic growth. 

Government Intervention: Measured by local government's general budget revenue, reflecting financial capacity 
and support for agricultural development. 

3.2.4 Mechanism Variables 

To analyze how modern agricultural parks influence agricultural economic growth, this study examines two 
mechanisms: technological innovation and rural industrial integration. 

Technological Innovation: The number of patent applications in a county is used to assess technological 
innovation, as it reflects the intensity of scientific and technological activities (Guo et al., 2023; Dong et al., 2023). 

Rural Industrial Integration: The proportion of the primary sector in a region's GDP is used to gauge the extent of 
rural industrial integration. A smaller proportion indicates a more diversified economic structure with enhanced 
synergies across agricultural, industrial, and service sectors, facilitating extended agricultural value chains and 
efficiency improvements. 

For comparability and to neutralize scale effects, all variables except the core binary explanatory variable and the 
rural industrial integration index have been log-transformed. 

3.3 Data Sources 

The data for this study are derived from non-panel data spanning 2007 to 2021 for 438 counties across four 
provinces in southwest China: Sichuan, Guizhou, Yunnan, and Chongqing. The dataset includes: 

Modern Agricultural Parks: A list of modern agricultural parks, sourced from the website of the Chinese Ministry 
of Science and Technology. 

Patent Applications: Data on patent applications, sourced from the China National Intellectual Property 
Administration (CNIPA) patent search and analysis platform. 

Control Variables: Data on agricultural labor, machinery, land inputs, farmers' income, agricultural output, and 
government revenue, sourced from the China County Statistical Yearbook. For missing values, the data were 
supplemented using local city and county statistical yearbooks, and linear interpolation was applied to impute any 
remaining missing values. 

Notably, due to severe data missingness and difficulties in collection, data from the Tibet Autonomous Region 
were excluded from the analysis of the southwest region. 

Table 1 presents the definitions and descriptive statistics of the variables used in the study. 

 



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Table 1. Variable Definitions and Descriptive Statistics 

 

4. Empirical Results Analysis 

4.1 Analysis of Baseline Regression Results 

Table 2 reports the estimation results of the baseline regression examining the impact of modern agricultural 
park construction on agricultural economic development. Column (1) does not include control variables, 
while columns (2) to (7) progressively add control variables. All equations employ a multi-period difference-
in-differences (DID) model for parameter estimation, with two-way fixed effects controlled. Based on the 
sign of the correlation coefficients and the statistical significance of the variables, the estimated results of 
the core explanatory variable across all columns are significant at the 1% level. 

In column (1) of Table 2, only the agricultural economic level is regressed. The results show a positive effect 
at the 1% significance level, indicating that in counties where modern agricultural parks are established, the 
agricultural economic level has increased by an average of 6.23%. When control variables are gradually 
introduced in columns (2) to (7), we find that the regression results of the interaction term remain highly 
significant, and the impact coefficient increases from 0.0623 to 0.0862. This increase reflects that the positive 
impact of modern agricultural parks on agricultural economic development is not only significant but also 
robust after controlling for other relevant factors. Therefore, we can confirm that Hypothesis 1 holds, i.e., 
the establishment of modern agricultural parks has a significant positive impact on agricultural economic 
growth. By establishing modern agricultural parks, we can effectively integrate sci-tech innovation resources, 
promote the development and transformation of agricultural technologies, and thereby improve agricultural 
production efficiency and product quality. 

Variable 

Category 
Variable Name Variable Description Mean 

Standard 

Deviation 

Dependent 

Variable 

Agricultural 

Economic Level 

Logarithm of the total output value of 

agriculture, forestry, animal husbandry, and 

fishery (in 10,000 CNY) 

12.02 1.152 

Core 

Variable  
Interaction Term 

Whether a modern agricultural park is 

established: Yes = 1, No = 0 
0.128 0.334 

Control 

Variables 

Agricultural Labor 

Level 

Logarithm of the number of employees in 

agriculture, forestry, animal husbandry, and 

fishery (in persons) 

11.53 0.684 

Agricultural 

Mechanization Level 

Logarithm of the total power of agricultural 

machinery (in kilowatts) 
2.983 0.807 

Agricultural Land 

Input 

Logarithm of the total sown area of crops 

(in hectares) 
3.729 0.933 

Farmers' Income 

Level 

Logarithm of the per capita disposable 

income of rural residents (in CNY) 
8.876 0.623 

Grain Output Level 
Logarithm of the total output value of grain 

(in tons) 
11.52 1.394 

Degree of Local 

Government 

Intervention 

Logarithm of local government general 

budget revenue (in 10,000 CNY) 
10.66 1.301 

Mediating 

Variables 

Regional Sci-Tech 

Innovation Level 

Logarithm of the number of patent 

applications in each region 
3.456 1.922 

Rural Industrial 

Integration 

Proportion of the added value of the 

primary industry to regional GDP 
0.289 0.204 



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Regarding the control variables, it is observed that only the degree of local government intervention has a negative 
impact. This could be because local governments, in their pursuit of increased fiscal budget revenue, have adopted 
more aggressive tax collection measures, such as raising agricultural tax rates or strengthening tax enforcement. 
These measures may increase production costs and economic burdens for farmers. 

 

Table 2. Regression Results of the Effect of Establishing Modern Agricultural Parks on Agricultural Economic 
Level 

Variable 
Dependent Variable: Agricultural Economic Level 

(1) (2) (3) (4) (5) (6) (7) 

Interaction Term 
0.0623*** 0.0724*** 0.0739*** 0.0757*** 0.0932*** 0.0870*** 0.0862*** 

(0.001) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) 

Agricultural Labor Level 
 0.588*** 0.544*** 0.540*** 0.250*** 0.127*** 0.125*** 

 (0.000) (0.000) (0.000) (0.000) (0.004) (0.005) 

Agricultural Mechanization 

Level 

  0.321*** 0.293*** 0.244*** 0.181*** 0.183*** 

  (0.000) (0.000) (0.000) (0.000) (0.000) 

Agricultural Land Input 
   0.488*** 0.510*** 0.231*** 0.252*** 

   (0.000) (0.000) (0.006) (0.004) 

Farmers' Income Level 
    0.611*** 0.551*** 0.556*** 

    (0.000) (0.000) (0.000) 

Grain Output Level      
0.306*** 

(0.000) 

0.306*** 

(0.000) 

Degree of Local Government 

Intervention 

      -0.0969*** 

      (0.000) 

Year Fixed Effects YES YES YES YES YES YES YES 

County Fixed Effects YES YES YES YES YES YES YES 

Constant 
12.01*** 5.234*** 4.784*** 3.085*** 1.073 0.714 1.658*** 

(0.000) (0.000) (0.000) (0.000) (0.103) (0.232) (0.005) 

Observations 6570 6570 6570 6570 6570 6570 6568 

R² 0.938 0.939 0.942 0.943 0.949 0.950 0.954 

Notes.*, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively. 

Standard errors in parentheses are robust standard errors. 

 

4.2 Parallel Trend Test 

To confirm the validity and accuracy of the multi-period difference-in-differences (DID) model, this study follows 
the method proposed by Beck et al. (2010) to test the parallel trends before and after the establishment of modern 
agricultural parks. A dynamic trend test graph is plotted to visually present the results. In this test, data from eight 
years before the policy implementation to six years after the policy implementation are selected, with the year 
before the policy implementation set as the baseline year. 

As shown in Figure 1, the estimated coefficients for most years before the policy implementation are not 
statistically significant and fluctuate widely around zero. However, after the policy implementation, the estimated 
coefficients for each year become significant and exhibit an upward trend, with all coefficients located above zero. 
This indicates that there is no significant difference between the treatment group and the control group before the 
policy implementation. The results demonstrate that the treatment group and the control group satisfy the parallel 
trend assumption, validating the applicability of the multi-period DID model in this study. 



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Figure 2. Parallel Trend Test 

 

4.3 Placebo Test 

To address the potential influence of unobservable factors at the county-year level on the estimation results, despite 
the inclusion of two-way fixed effects in the baseline regression to control for heterogeneity across counties and 
years, this study adopts the approach of Tang Haodan et al. (2022) to conduct a placebo test. Specifically, counties 
and policy implementation years are randomly assigned to generate a pseudo-core explanatory variable, which is 
then used for regression analysis. 

Figure 3 illustrates the results of 500 random sampling iterations. As shown, the majority of the 500 estimated 
coefficients are distributed around zero rather than clustering around the true estimated result of 0.0861. 
Additionally, the p-values for most of these coefficients are statistically insignificant. This indicates that the true 
estimated result in this study is a clear outlier within the sampling distribution and is unlikely to have occurred by 
chance. Thus, the placebo test confirms that the agricultural economic growth effect attributed to the modern 
agricultural park policy is genuine and not influenced by unobservable factors at the county-year level. 

 
 

Figure 3. placebo test 

p
olicy effect  

policy implementation time point 

k
ern

eld
en

sity

p-value 

kernel density of 

estimated 

p-value 

estimated coefficient



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4.4 Robustness Test 

Given that the establishment of modern agricultural parks is not a random process but rather a decision based on 
comprehensive evaluations by the Ministry of Science and Technology or provincial science and technology 
departments, considering factors such as regional agricultural development levels, the status of specialized 
agriculture, the scale of technological investment, and scientific research and innovation capabilities, there is an 
inherent difference between the treatment and control groups. This inevitably leads to sample selection bias. To 
address the potential self-selection bias in the policy effect evaluation using the difference-in-differences (DID) 
method, this study employs the propensity score matching-difference-in-differences (PSM-DID) approach (Table 
3). By matching each treatment group sample with a control group sample that has a similar propensity score, the 
robustness test ensures that the evaluation results are more reliable and accurate. 

In Table 3, column (1) presents the baseline regression results, primarily for comparison with the subsequent 
columns. Column (2) shows the regression results using samples with non-empty weights. By including these 
samples in the matching process, they are incorporated into the DID regression model for parameter estimation, 
thereby mitigating the selection bias present in the baseline regression to some extent. Column (3) displays the 
regression results using samples that satisfy the common support assumption, while column (4) presents the 
frequency-weighted regression analysis results, considering the importance of sample weights. Notably, all four 
estimated coefficients are statistically significant at the 1% level, reaffirming that the establishment of modern 
agricultural parks has a significant positive impact on improving local agricultural economic levels. 

 

Table 3. PSM-DID Robustness Test 

 

Variable 

(1) (2) (3) (4) 

Fixed Effects Regression Weight!=. On_Support Weight_Reg 

Interaction Term 
0.0862*** 0.181*** 0.0875*** 0.184*** 

(0.000) (0.000) (0.000) (0.000) 

Agricultural Labor Level 
0.125*** 0.0737 0.0959** 0.180 

(0.005) (0.555) (0.031) (0.113) 

Agricultural Mechanization Level 
0.183*** 0.205*** 0.184*** 0.175*** 

(0.000) (0.002) (0.000) (0.002) 

Agricultural Land Input 
0.252*** -0.171 0.254*** -0.106 

(0.004) (0.329) (0.003) (0.493) 

Farmers' Income Level 
0.556*** 0.655*** 0.564*** 0.631*** 

(0.000) (0.000) (0.000) (0.000) 

Grain Output Level 
0.306*** 0.153*** 0.303*** 0.145*** 

(0.000) (0.003) (0.000) (0.003) 

Degree of Local Government Intervention 
-0.0969*** -0.0892*** -0.0973*** -0.103*** 

(0.000) (0.000) (0.000) (0.000) 

Constant 
1.658*** 4.559*** 1.949*** 3.658** 

(0.005) (0.004) (0.001) (0.013) 

Year Fixed Effects YES YES YES YES 

County Fixed Effects YES YES YES YES 

Observations 6568 2103 6545 2475 

R2 0.954 0.961 0.954 0.963 

Notes.*, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively. 

Standard errors in parentheses are robust standard errors. 

 

 



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4.5 Mechanism Analysis 

4.5.1 The Impact of Modern Agricultural Parks on Agricultural Economy Through Technological Innovation 

Column (2) of Table 4 presents the estimated results of the impact of establishing modern agricultural parks on 
regional technological innovation levels. The data show that the establishment of modern agricultural parks 
significantly promotes regional technological innovation capabilities by effectively aggregating innovation 
resources such as high-tech talent, specialized technologies, and financial support, thereby enhancing agricultural 
technological levels. In column (3), although the estimated coefficient of the interaction term is significant at the 
1% level, the impact of regional technological innovation capabilities on local agricultural economic development 
is not statistically significant. To further verify the existence of the mediating effect, the Sobel test or bootstrap 
test was employed. Given the statistical power of the bootstrap test and the sample size of this study, the bootstrap 
method was chosen. By resampling the original sample with replacement and repeating the process 1,000 times, 
the results in Table 5 indicate that the total effect of modern agricultural parks on regional agricultural economic 
levels is 0.326, of which the direct effect is 0.16 and the indirect effect is 0.165, accounting for 50.61% of the total 
effect. Furthermore, both the confidence interval (P) and the bias-corrected confidence interval (BC) do not include 
zero, indicating that the mediating effect is significant. Therefore, we can conclude that Hypothesis 2 holds. 

 

Table 4. The Impact of Modern Agricultural Parks on Agricultural Economy Through Technological Innovation 

Variable  

(1) (2) (3) 

Baseline 

Regression 

Regional Technological 

Innovation Level 

Agricultural Economic 

Level 

Interaction Term 
0.0862*** 0.253*** 0.0849*** 

(0.000) (0.000) (0.000) 

Regional Technological 

Innovation Level 

  0.00505 

  (0.195) 

Control Variables Included Included Included 

Year Fixed Effects YES YES YES 

County Fixed Effects YES YES YES 

Constant 
1.658*** 0.573 1.656*** 

(0.005) (0.856) (0.005) 

Observations 6568 6568 6568 

R2 0.954 0.738 0.954 

Notes.*, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively. 

Standard errors in parentheses are robust standard errors. 

 

Table 5. Bootstrap Test Results 

Test Method 
Regression 

Coefficient 
Bias 

Standard 

Error 

95% Confidence 

Interval 
 

Lower 

Bound 

Upper 

Bound 
 

Indirect Effect 0.16568611 -0.0009081 0.01533964 0.1361087 0.1956324 (P) 

    0.137309 0.197297 (BC) 

Direct Effect 0.16031955 0.0011065 0.035686 0.0907738 0.2314546 (P) 

    0.0918679 0.2317094 (BC) 

 

 

 



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4.5.2 The Impact of Modern Agricultural Parks on Agricultural Economy Through Rural Industrial Integration 

Column (2) of Table 6 presents the estimated results of the effect of establishing modern agricultural parks on 
rural industrial integration. The estimated coefficient is positive and significant at the 5% level, indicating that 
although the establishment of modern agricultural parks does not significantly enhance the level of rural industrial 
integration, it does help increase the proportion of the primary industry in the local economy, relatively reducing 
the output value of the secondary and tertiary industries, thereby promoting agricultural development. In column 
(3), the estimated coefficient results show that establishing modern agricultural parks cannot drive regional 
agricultural economic growth by promoting industrial integration but can only promote agricultural economic 
development by increasing the proportion of the primary industry in the regional economy. Therefore, Hypothesis 
3 is not validated. This may be because modern agricultural parks overly focus on the research and innovation of 
agricultural product technologies while failing to effectively integrate subsequent stages such as agricultural 
product processing and sales services, resulting in an incomplete industrial chain and thus hindering the integrated 
development of rural industries. 

 

Table 6. The Impact of Modern Agricultural Parks on Agricultural Economy Through Rural Industrial Integration 

Variable 

(1) (2) (3) 

Baseline Regression 
Rural Industrial 

Integration 
Agricultural Economic 

Level 

Interaction Term 
0.0862*** 0.0100** 0.0832*** 

(0.000) (0.022) (0.000) 

Rural Industrial 
Integration 

  0.299*** 

  (0.000) 

Control Variables Included Included Included 

Year Fixed Effects YES YES YES 

County Fixed 
Effects 

YES YES YES 

Constant 
1.658*** 2.029*** 1.052* 

(0.005) (0.000) (0.073) 

Observations 6568 6568 6568 

R2 0.954 0.893 0.954 

Notes.*, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively. 

Standard errors in parentheses are robust standard errors. 

 

4.6 Heterogeneity Analysis 
4.6.1 Analysis by Administrative Level 

The regression results in columns (1) and (2) of Table 7 show that the coefficients for both provincial capital and 
non-provincial capital groups are significant at the 1% level. To better understand the differences between 
provincial capitals and non-provincial capitals, a Chow test was conducted to examine the coefficient differences 
between the groups. The results, presented in Table 8, show that the p-value for the interaction term between the 
provincial capital variable and the core explanatory variable (whether a modern agricultural park is established) is 
0.000, which is significant at the 1% level. This confirms that the coefficient differences between the groups are 
significant, supporting Hypothesis 4. The impact of modern agricultural parks on promoting agricultural economic 
development differs significantly between provincial capitals and non-provincial capitals. Based on the estimated 
coefficients, establishing modern agricultural parks in provincial capitals yields a 28% benefit for agricultural 
economic growth. This is because provincial capitals are typically regional political, economic, and cultural 
centers, making it easier to secure government policy support and resource allocation, including funding, 
technology, and talent. 

 



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Table 7. Heterogeneity Analysis Based on Administrative Level, Core Metropolitan Areas in the Southwest, and 
Major Grain-Producing Counties 

Variable 

(1) (2) (3) (4) (5) (6) 

Provincial 
Capitals 

Non-
Provincial 
Capitals 

Core 
Metropolitan 

Areas 

Non-Core 
Metropolitan 

Areas 

Major Grain-
Producing 
Counties 

Non-Major 
Grain-

Producing 
Counties 

Interaction 
Term 

0.286*** 0.0546*** 0.213*** 0.00784 -0.0387** 0.0788*** 

(0.000) (0.000) (0.000) (0.657) (0.028) (0.000) 

Control 
Variables 

Included Included Included Included Included Included 

Constant 
3.545* 4.619*** -1.882* 6.497*** 11.42*** 1.593** 

(0.077) (0.000) (0.081) (0.000) (0.000) (0.026) 

Year Fixed 
Effects 

YES YES YES YES YES YES 

County Fixed 
Effects 

YES YES YES YES YES YES 

Observations 808 5760 1648 4920 1710 4858 

R² 0.955 0.955 0.959 0.954 0.979 0.962 

Notes.*, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively. 

Standard errors in parentheses are robust standard errors. 

 

Table 8. Coefficient Difference Test Between Groups (Chow Test) 

Variable 
(1) 

Agricultural Economic Level 

1.provincial_capital#c.did 
0.432*** 

(0.000) 

Control Variables Included 

Constant 
12.07*** 

(0.000) 

Year Fixed Effects YES 

County Fixed Effects YES 

Observations 6570 

R² 0.943 

 

4.6.2 Analysis by Core Metropolitan Areas in the Southwest 

Based on the Chengdu Metropolitan Area Development Plan, Chongqing Metropolitan Area Development Plan, 
Guiyang-Gui'an-Anshun Metropolitan Area Development Plan, and Yunnan Provincial Territorial Spatial Plan 
(2021-2035), 22 cities, including Chengdu, Deyang, Meishan, Ziyang, Yuzhong District, Dadukou District, 
Jiangbei District, Guiyang, Gui'an, Anshun, and Kunming, are identified as core metropolitan areas. Columns (3) 
and (4) of Table 7 show that establishing modern agricultural parks in core metropolitan areas significantly 
improves agricultural economic levels. This is likely because core metropolitan areas are home to numerous high-
quality higher education institutions, top talent, and research institutes, which provide robust technical support and 
talent reserves for the parks. These resources facilitate the transformation and practical application of agricultural 
scientific and technological achievements. Additionally, establishing modern agricultural parks in core 
metropolitan areas can attract related agricultural enterprises, research institutions, and service providers, forming 



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a complete industrial chain. This agglomeration effect promotes resource sharing, information exchange, and 
technical collaboration, further enhancing the overall competitiveness of agriculture. 

4.6.3 Analysis by Major Grain-Producing Counties 

Sichuan Province, the only core grain-producing region in the southwest, has launched the "Tianfu Granary: 
Hundred Counties, Thousand Zones" Construction Action Plan (2024–2026), aiming to create 1,000 high-
standard, high-yield grain and oil demonstration zones in over 100 counties and districts. This study categorizes 
counties in the southwest based on whether they are major grain-producing counties and employs heterogeneity 
analysis. From the regression coefficients in columns (5) and (6) of Table 7, it is evident that modern agricultural 
parks in major grain-producing counties have a significant negative impact on agricultural economic growth, while 
in non-major grain-producing counties, the impact is positive and significant at the 1% level. This suggests that 
establishing modern agricultural parks in non-major grain-producing counties can promote regional agricultural 
economic growth. The primary reason may be that major grain-producing counties already have well-established 
agricultural production systems and high resource utilization levels. Establishing modern agricultural parks in 
these areas could lead to competition for core resources such as land, water, and labor, potentially causing 
imbalanced resource allocation and negatively affecting traditional grain production efficiency and scale. In 
contrast, non-major grain-producing counties have lower resource utilization efficiency and lagging agricultural 
production. Introducing modern agricultural parks in these areas can bring new management practices and 
technologies, significantly improving resource use efficiency and driving agricultural economic growth. 

5. Conclusions and Implications 

5.1 Research Conclusions 

This study provides a detailed analysis of the role of modern agricultural parks in promoting regional agricultural 
economic growth and concludes the following: 

Significant Positive Impact: Regardless of whether control variables are included, the regression coefficient 
between modern agricultural parks and agricultural economic growth is significant at the 1% level. This confirms 
that modern agricultural parks substantially contribute to regional agricultural economic growth. 

Robustness of Results: The accuracy and reliability of the regression results are ensured through parallel trend 
tests, placebo tests, and PSM-DID robustness tests, further validating the causal relationship. 

Mechanism Analysis: Modern agricultural parks significantly promote agricultural economic development by 
enhancing regional technological innovation capabilities. However, this effect is not achieved through rural 
industrial integration. 

Heterogeneity Analysis: The impact of modern agricultural parks on agricultural economic growth varies across 
administrative levels, core metropolitan areas, and major grain-producing counties. Specifically: 

Provincial Capitals: The most significant positive impact is observed in provincial capitals. 

Non-Provincial Capitals: Positive effects are also found in non-provincial capital regions. 

Core Metropolitan Areas: Modern agricultural parks in core metropolitan areas contribute positively to agricultural 
economic growth. 

Non-Grain-Producing Counties: These regions also benefit from the establishment of modern agricultural parks. 

In summary, the establishment of modern agricultural parks has a positive impact on agricultural economic growth, 
with the most pronounced effects in provincial capitals. 

5.2 Policy Implications 

Based on the research findings, this study proposes the following policy recommendations: 

1. Leverage the Growth Wisdom of Modern Agricultural Parks to Consolidate and Enhance Their Quality, 
Maximizing Their Role in Promoting Agricultural Economic Prosperity 

Specifically, the southwestern region should build on the successful examples and models of existing modern 
agricultural parks to further strengthen their construction. This includes improving support policies and evaluation 
mechanisms, establishing a performance-based dynamic assessment system, and implementing promotion and exit 
mechanisms. Additionally, it is essential to encourage collaboration between municipal-level modern agricultural 
parks and higher-level parks in the industrial chain, positioning modern agricultural parks as pioneers in regional 
agricultural industrial upgrading and driving the revitalization of rural economies. 



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2. Further Enhance the Technological Innovation Capacity of Modern Agricultural Parks to Promote Efficient and 
High-Quality Agricultural Development 

By leveraging the development and application of new technologies, modern agricultural parks can lead the 
transformation and upgrading of local agricultural economies. The southwestern region should address technical 
bottlenecks in agricultural production by enhancing agricultural technical support. Advanced agricultural 
technologies and management models should be widely applied in rural areas to improve agricultural productivity 
and product quality, thereby increasing farmers’ income and accelerating the modernization of agriculture. 

3. Develop Leading Agricultural Industries and Build a Robust Modern Agricultural Industrial System 

The fundamental goal of the rural revitalization strategy is industrial prosperity. Therefore, innovation should be 
the driving force to accelerate agricultural modernization and fully implement the rural revitalization strategy, 
which should be the core purpose of building modern agricultural parks. By constructing high-level modern 
agricultural parks, pooling superior resources, and advancing the research and development of key technologies, 
the transformation and dissemination of scientific and technological achievements can be accelerated. This will 
cultivate a series of leading agricultural industries with demonstration and driving functions, injecting new vitality 
into agricultural modernization and creating broader opportunities for rural revitalization. 

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    /HRV (Za stvaranje Adobe PDF dokumenata najpogodnijih za visokokvalitetni ispis prije tiskanja koristite ove postavke.  Stvoreni PDF dokumenti mogu se otvoriti Acrobat i Adobe Reader 5.0 i kasnijim verzijama.)
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    /NLD (Gebruik deze instellingen om Adobe PDF-documenten te maken die zijn geoptimaliseerd voor prepress-afdrukken van hoge kwaliteit. De gemaakte PDF-documenten kunnen worden geopend met Acrobat en Adobe Reader 5.0 en hoger.)
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    /ENU (Use these settings to create Adobe PDF documents best suited for high-quality prepress printing.  Created PDF documents can be opened with Acrobat and Adobe Reader 5.0 and later.)
  >>
  /Namespace [
    (Adobe)
    (Common)
    (1.0)
  ]
  /OtherNamespaces [
    <<
      /AsReaderSpreads false
      /CropImagesToFrames true
      /ErrorControl /WarnAndContinue
      /FlattenerIgnoreSpreadOverrides false
      /IncludeGuidesGrids false
      /IncludeNonPrinting false
      /IncludeSlug false
      /Namespace [
        (Adobe)
        (InDesign)
        (4.0)
      ]
      /OmitPlacedBitmaps false
      /OmitPlacedEPS false
      /OmitPlacedPDF false
      /SimulateOverprint /Legacy
    >>
    <<
      /AddBleedMarks false
      /AddColorBars false
      /AddCropMarks false
      /AddPageInfo false
      /AddRegMarks false
      /ConvertColors /ConvertToCMYK
      /DestinationProfileName ()
      /DestinationProfileSelector /DocumentCMYK
      /Downsample16BitImages true
      /FlattenerPreset <<
        /PresetSelector /MediumResolution
      >>
      /FormElements false
      /GenerateStructure false
      /IncludeBookmarks false
      /IncludeHyperlinks false
      /IncludeInteractive false
      /IncludeLayers false
      /IncludeProfiles false
      /MultimediaHandling /UseObjectSettings
      /Namespace [
        (Adobe)
        (CreativeSuite)
        (2.0)
      ]
      /PDFXOutputIntentProfileSelector /DocumentCMYK
      /PreserveEditing true
      /UntaggedCMYKHandling /LeaveUntagged
      /UntaggedRGBHandling /UseDocumentProfile
      /UseDocumentBleed false
    >>
  ]
>> setdistillerparams
<<
  /HWResolution [2400 2400]
  /PageSize [612.000 792.000]
>> setpagedevice

