Frontiers in Business, Economics and Management 
ISSN: 2766-824X | Vol. 15, No. 1, 2024 

 

382 

Study	on	the	Impact	of	Agricultural	Supply	Chain	
Finance	on	Poverty	Reduction:	An	Empirical	Analysis	
Based	on	Nine	Provinces	in	China's	Border	Region	
Wenjing Li*, Lingjun Xie 

School of Economics, Guangxi University, Nanning 530000, China 
* Corresponding author 

 

Abstract: This paper employs panel data from nine provinces and regions in China's border areas from 2011 to 2021 to 
empirically test the impact of agricultural supply chain finance in China's border areas on poverty reduction. The intermediary 
mechanism test model is used to investigate the underlying mechanisms. The findings of this research indicate that the 
development of agricultural supply chain finance has a significant effect on improving per capita disposable income in rural 
areas. There is, however, heterogeneity in the impact of this development across different border provinces and regions. The 
Southwest Border Region has the most pronounced effect in improving per capita disposable income in rural areas through 
agricultural supply chain finance. This research result provides strong theoretical support for the development of agricultural 
supply chain finance in China's border areas and also provides important reference basis for relevant policy formulation. As 
further research is conducted, it is anticipated that more results will emerge, which will facilitate the advancement of economic 
development and poverty reduction initiatives in China's border regions. 

Keywords: China's Border Region, Agricultural Supply Chain Finance, Poverty Reduction. 
 

1. Introduction 
The implementation of agricultural supply structure reform 

and rural revitalisation strategy has led to a growing focus on 
the relationship between agricultural supply chain finance and 
rural poverty alleviation. Agricultural supply chain finance 
(ASCF) has become an essential component of rural 
revitalisation and the construction of a modern industrial 
system. 

In recent years, numerous regions both within and beyond 
China have begun to introduce the concept of supply chain 
finance (SCF) into the agricultural field. This has led to a 
gradual realisation of the significant significance of SCF for 
rural finance. Some scholars posit that SCF represents the 
pinnacle of innovative rural financial solutions (Zhou et al., 
2018) [1], while for small farmers, SCF represents the pinnacle 
of their financing plan evolution (Renée, 2014; Gashayie et 
al., 2015) [2] [3]. The rationale for this approach lies in the fact 
that Agricultural Supply Chain Finance (ASCF) can not only 
effectively alleviate credit constraints on farmers, but more 
importantly, it can achieve transformation, commercialisation, 
and sustainable development of agriculture[4]. This, in turn, 
will promote small farmers to develop upstream in the supply 
chain, and avoid their exclusion from the mainstream value 
chain under the trend of economic globalization[5]. 

It can be argued that ASCF is not only a key means to 
alleviate credit constraints on farmers, but also a good 
solution to promote agricultural production systems driven by 
small farmers. This financial model, which combines SCF 
and agriculture, is expected to provide new ideas and support 
for rural economic development and agricultural 
modernization. It is therefore of positive significance in 
promoting stable income growth and improving living 
conditions for small farmers. As the field of agricultural 
research and practice continues to evolve, ASCF is poised to 
assume an increasingly pivotal role in the agricultural sector, 

offering a promising avenue for addressing rural financial 
challenges. 

This study seeks to examine the potential of agricultural 
supply chain finance (ASCF) in alleviating poverty and, 
based on this analysis, propose targeted policy 
recommendations and implementation strategies. This study 
will investigate the impact of agricultural supply chain 
finance on poverty reduction through a comprehensive 
examination of practical cases and economic data. It will 
identify the positive effects of agricultural supply chain 
finance on poverty alleviation and elucidate the mechanisms 
and influencing factors. 

2. Theoretical Hypothesis  
The process of rural development has been facilitated by 

rural finance, which has promoted rural economic 
development and played an important role in rural 
revitalisation and rural supply structure adjustment. 
Agricultural supply chain finance represents a novel 
operational model that has emerged in recent years. This 
model provides financial support and risk management 
services to various links in the agricultural supply chain 
through financial means, with the objective of promoting the 
development of agricultural production, processing, 
circulation, and related industries. The implementation of 
agricultural supply chain finance has the potential to enhance 
farmers' income by reducing capital costs and expanding loan 
channels for farmers in border provinces and regions of China. 
This, in turn, could lead to an increase in per capita disposable 
income in rural areas of the nine provinces and regions in the 
border region. Hypothesis 1, therefore, proposes that 
agricultural supply chain finance can directly promote the 
increase of per capita disposable income in rural areas of the 
nine provinces and regions in the border region. 

The implementation of digital inclusive finance has the 
potential to significantly reduce the cost of financial services, 



 

383 

improve the efficiency of financial services, and help alleviate 
the phenomenon of "financial exclusion" in rural areas of 
border provinces. This is due to the fact that digital inclusive 
finance can better conduct credit queries and ratings, covering 
a wider range of user groups. Furthermore, the development 
of agricultural supply chain finance has the potential to 
significantly reduce poverty. In order to further investigate 
whether the level of digital finance development has a 
moderating or partially mediating effect on the poverty 
reduction effect of agricultural supply chain finance, this 
article proposes hypothesis 2: Agricultural supply chain 
finance effectively promotes the increase of per capita 
disposable income in rural areas of China's border provinces 
through the transmission path of digital finance. 

A significant proportion of migrant workers elect to return 
to their place of origin to establish businesses, which 
stimulates the growth of the rural economy, enhances farmers' 
income, and also enhances their quality of life. Concurrently, 
the integrated development of the three industries has 
effectively enhanced the utilisation rate of resources and 
factors in rural areas. This has involved the incorporation of 
agriculture into a broader social division of labour, enabling 
farmers to participate in non-agricultural work during non-
agricultural seasons. This has resulted in increased income for 
farmers, and has facilitated the process of urban-rural 
integration. Based on the above analysis, hypothesis 3 is 
proposed: Agricultural supply chain finance increases per 
capita disposable income in rural areas by influencing 
urbanisation rates. 

3. Research Design 

3.1. Model Setting  
This article constructs a benchmark model to explore the 

effect of agricultural supply chain finance on the per capita 
disposable income and Engel's coefficient of rural residents 
in China: 

 
𝑙𝑛𝑃𝐼 𝛼 𝛼 𝑙𝑛𝐴𝑆𝐶𝐹 , 𝛼 ∑ 𝑋 , 𝑢 𝜀      (1) 

 
This study is based on the analysis of the mechanism of 

agricultural supply chain finance in Part 2 and the proposed 
research hypothesis. It explores the transmission mechanism 
of agricultural supply chain finance on poverty reduction in 
nine provinces and regions of China's border areas through 
the following model. 

 
𝑙𝑛𝑃𝐼 𝛽 𝛽 𝑙𝑛𝐴𝑆𝐶𝐹 , 𝛽 𝐾 𝑤         (2) 

 
𝑙𝑛𝐷𝐼𝐹 𝛿 𝛿 𝑙𝑛𝐴𝑆𝐶𝐹 𝛿 𝐾 𝑤        (3) 

 
𝑙𝑛𝑃𝐼 𝜃 𝜃 𝑙𝑛𝐴𝑆𝐶𝐹 𝜃 𝐷𝐼𝐹 𝜃 𝐾 𝑤   (4) 

 
𝑙𝑛𝑈𝑅 𝜆 𝜆 𝑙𝑛𝐴𝑆𝐶𝐹 𝜆 𝐾 𝑤        (5) 

 
𝑙𝑛𝑃𝐼 𝜃 𝜃 𝑙𝑛𝐴𝑆𝐶𝐹 𝜃 𝑈𝑅 𝜃 𝐾 𝑤    (6) 

 
The  𝑙𝑛𝑃𝐼   represents the per capita disposable income 

level of rural areas in nine provinces and regions, the 
𝑙𝑛𝐴𝑆𝐶𝐹 ,   represents the level of agricultural supply chain 
finance development in each province and region, the 𝑈𝑅  
represents the urbanisation rate in Each province and region 
is represented by 𝐷𝐼𝐹  , which denotes the level of digital 
finance development in that area. 𝐾  is the control variable, 

while α、β、δ、θ、λ represent the relevant coefficients to be 
estimated. Finally, ω is the error term. 

To test for a mediating effect using the above model, it is 
first necessary to examine the overall effect of agricultural 
supply chain finance on the dependent variable, that is, the 
coefficient in (2) 𝛽  . The significance of 1 should then be 
tested. Secondly, the coefficients in (3) and (4) 𝛿 、 𝜃  
should be tested, with significance being used. To verify 
whether there is a mediating effect of the development level 
of digital finance, the coefficients in (5) and (6) (𝜆 、 𝜃 ) 
must be verified once more. The significance of 2 is used to 
test the mediating effect of the urbanisation rate. Finally, the 
coefficients in (4) and (6) 𝜃  must be checked. The 
significance Finally, through stepwise regression and 
bootstrap testing, it was found that the development level of 
digital finance plays a completely mediating role in the role 
of agricultural supply chain finance in poverty reduction in 
the nine provinces and regions of China's border areas (4). 
The urbanisation rate plays a partial intermediary role in (6). 

3.2. Variable selection  
3.2.1. The dependent variable 

This article selects the per capita disposable income (PI) of 
rural residents and measures this indicator using the 
standardized result of taking the logarithm of rural per capita 
disposable income.  

3.2.2. The explanatory variables 
The explanatory variables are the Development Level of 

Agricultural Supply Chain Finance (ASCF), which is 
measured by the balance of agricultural loans in different 
provinces and regions. This indicator effectively reflects the 
level of financial support and development in rural areas by 
various financial institutions in rural areas. Therefore, this 
article selects the balance of agricultural loans in nine 
provinces and regions of China as the main explanatory 
variable.  

3.2.3. Adjusting variables 
The level of digital finance development (DIF) is measured 

by the inclusive finance development indicators of China's 
nine border provinces and regions. 

The ratio of foreign direct investment in each province and 
region (converted at the average exchange rate of USD to 
RMB in the current year) to the GDP of each province and 
region is employed. 

3.2.4. The control variable 
The following variables are employed in the analysis: 

population density (population), expressed as the number of 
people per unit area in each province and region; government 
intervention degree (gov), defined as the proportion of fiscal 
expenditure in each province and region to GDP; 
mechanisation level (mac), expressed as total mechanical 
power/rural population (10000 kilowatts/10000 people); and 
industrial structure (indu), defined as the output value of the 
tertiary industry/output value of the secondary industry. 

3.3. Data Source 
This paper selects nine border provinces and regions in 

China as the research objects. The data is derived from the 
China Statistical Yearbook, the China Financial Yearbook, 
and statistical yearbooks of provinces and regions from 2011 
to 2021. The digital finance development level is adopted 
from the Inclusive Finance Research Center of China Institute 
of Digital Finance, Peking University. Furthermore, in order 



 

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to avoid heteroscedasticity and reduce errors, logarithmic 
preprocessing was performed on some variables. The 

resulting descriptive statistical results are presented in Table 
1. 

 
Table 1. Descriptive Statistical Results 

 Variable Obs Mean Std. Dev. Min Max 
Dependent variable lnPI 99 9.236 .343 8.361 9.864 

Explanatory variables lnASCF 99 8.437 .941 4.366 9.451 
Adjusting variables lnDIF 99 5.174 .733 2.786 5.878 

UR 99 .523 .12 .228 .728 
control variable indu 99 1.195 .373 .624 2.315 

fdi 99 .009 .013 0 .07 
gov 99 .391 .288 .156 1.334 

lnmac 99 7.856 0.621 6.059 8.841 
 

4. Empirical Analysis of Poverty 
Reduction Through Agricultural 
Supply Chain Finance 

4.1. Poverty reduction effect test  
A regression analysis was conducted on panel data from 

nine provinces in China's border areas, based on model (1), 

and it was found that the Hausman test significantly rejected 
the null hypothesis. In order to ensure the reliability of the 
empirical results, a fixed effects test model was ultimately 
used. This study employed the least squares method for 
dummy variable estimation (LSDV) with Liu fixed effects, 
fixed individual effects, and conducted benchmark regression 
tests. The results are presented in Table 2. 

 

Table 2. Benchmark Regression Results 

 (1) (2) 
 lnPI lnPI 

lnASCF 0.5202*** 0.3753*** 
 (4.0147) (8.9433) 

indu  0.3502** 
  (3.2252) 

fdi  -3.9819 
  (-1.7022) 

gov  -0.5439 
  (-1.8419) 

lnmac  0.3072* 
  (2.0701) 

_cons 4.8468*** 3.4879** 
 (4.4336) (3.0460) 

Individual fixation Yes Yes 
N 99 99 
R2 0.7470 0.9024 

t statistics in parentheses 
* p < 0.1, ** p < 0.05, *** p < 0.01 

 

4.2. Mechanism verification 
4.2.1. Digital inclusive finance 

When compared with the basic model, adjusting R2 
slightly increases after the addition of new variables, 
indicating that the added digital finance development 
indicators are effective and useful. The inclusion of digital 
financial indicators has improved the percentage of 
coefficient of variation that can be explained by regression 
equations. Firstly, the overall effect of agricultural supply 
chain finance on the dependent variable, i.e. the coefficient in 
Model (2) β_1. This indicates that the development of 
agricultural supply chain finance has a significant positive 

impact on the per capita disposable income of rural areas in 
various provinces and regions of China's border areas. 
Secondly, after incorporating digital financial indicators, the 
coefficients in model (4) θ_2 are significant at the 1% level, 
indicating a significant indirect effect of agricultural supply 
chain finance on poverty reduction. The results indicate that 
agricultural supply chain finance has a significant direct effect 
on increasing farmers' income. Furthermore, the development 
of digital finance (DIF) plays a partial mediating role in 
promoting the increase of per capita disposable income of 
rural farmers in agricultural supply chain finance. This 
supports the hypothesis previously mentioned. 

 
 
 
 
 



 

385 

Table 3. Transmission mechanism of agricultural supply chain finance in poverty reduction through the role of digital 
finance 

 (1) (2) (3) 
 lnPI DIF lnPI 

lnASCF 0.3753*** 106.8674*** 0.0520* 
 (8.9433) (5.7920) (2.0417) 

indu 0.3502** 119.9190*** -0.0126 
 (3.2252) (4.0761) (-0.2908) 

fdi -3.9819 -1.4e+03** 0.3872 
 (-1.7022) (-3.0142) (0.4562) 

gov -0.5439 -97.2886 -0.2496 
 (-1.8419) (-1.0053) (-1.5910) 

lnmac 0.3072* 131.3454** -0.0902* 
 (2.0701) (3.1298) (-2.1550) 

DIF   0.0030*** 
   (18.3708) 

_cons 3.4879** -1.8e+03*** 8.9728*** 
 (3.0460) (-5.1985) (37.3848) 

Individual fixation Yes Yes Yes 
N 99 99 99 
R2 0.9024 0.9162 0.9811 

t statistics in parentheses 
* p < 0.1, ** p < 0.05, *** p < 0.01 

 

4.2.2. Urbanisation rate 
When compared with the basic model, adjusting R2 

slightly increased after the addition of new variables, 
indicating that the increased urbanisation rate indicator is 
effective. Furthermore, stepwise regression and bootstrap 
methods were employed to test the mediating mechanism of 
urbanisation rate. 

Based on the test results of model (6), the coefficient of 
interaction between urbanisation rate and the dependent 
variable was obtained as shown in Table 4. It was found that 

the coefficient in model (5), λ1, was significant. Further 
analysis was conducted on the presence and magnitude of the 
mediating effect, and the bootstrap method was used to test 
the significance of the coefficient product. The results, as 
presented in Table 5, indicated that, at a 95% confidence 
interval excluding 0, the indirect effect was significant. This 
implies that the urbanisation rate was a significant mediating 
effect of agricultural supply chain finance on poverty 
reduction in the nine provinces of China's border regions. 
Hypothesis 3, previously mentioned, was thus confirmed. 

 
Table 4. Path transmission of urbanization rate in poverty reduction through agricultural supply chain finance 

 (1) (2) (3) 
 lnPI UR lnPI 

lnASCF 0.3753*** 0.0550*** 0.0805* 
 (8.9433) (7.3859) (2.1860) 

indu 0.3502** 0.0529** 0.0667** 
 (3.2252) (2.6970) (2.5698) 

fdi -3.9819 -0.1612 -3.1173*** 
 (-1.7022) (-0.4146) (-6.5336) 

gov -0.5439 -0.0362 -0.3498*** 
 (-1.8419) (-0.6669) (-4.1164) 

lnmac 0.3072* 0.0153 0.2254** 
 (2.0701) (0.5500) (3.1583) 

UR   5.3628*** 
   (7.8392) 

_cons 3.4879** -0.1077 4.0655*** 
 (3.0460) (-0.5330) (7.8657) 

Individual fixation Yes Yes Yes 
N 99 99 99 
R2 0.9024 0.8688 0.9765 

t statistics in parentheses 
* p < 0.1, ** p < 0.05, *** p < 0.01 

 
Table 5. Bootstrap results of the mediating effect of urbanization rate 

 
Observed 
coefficient 

Bootstrap 
Std. err. 

z p>|z| 
Normal-based 

[95% conf. interval] 
_bs_1 0.128 0.035 3.69 0.000 0.060 0.196 
_bs_2 0.161 0.035 4.83 0.000 0.099 0.235 



 

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4.2.3. Heterogeneity analysis 
The objective of this analysis is to verify whether there are 

differences in the impact of agricultural supply chain finance 
on poverty reduction in border provinces across different 
border regions. This article refers to the analysis of Sun 
Jiuwen et al., which divides China's land border into 
Northeast Border, Northwest Border, and Southwest Border. 
The results are shown in Table 6. The regression results 
indicate a certain degree of heterogeneity in the impact of 
agricultural supply chain finance on poverty reduction in 
border provinces and regions of China. In the southwestern 
border region, the development level of agricultural supply 
chain finance has a significant positive effect on improving 
rural per capita disposable income, with a significant 
coefficient at the 1% significance level and a coefficient of 

0.3929. This indicates that with the development of 
agricultural supply chain finance, for every unit increase, 
rural per capita disposable income increases by 0.3929 units. 

In the northwest border region, the development level of 
agricultural supply chain finance has a significant positive 
effect on improving the per capita disposable income of rural 
residents. The coefficient is significant at the 5% significance 
level, and the coefficient is 0.3807. This indicates that with 
the development of agricultural supply chain finance, for 
every unit increase, the per capita disposable income of rural 
residents increases by 0.3807 units. In the northeastern border 
region, with the development of agricultural supply chain 
finance, for every 1 unit increase, the per capita disposable 
income of rural residents increases by 0.2333 units. 
Compared with the southwestern and northwestern border 
regions, the poverty reduction effect has decreased. 

 
Table 6. Heterogeneity Regression Results 

 Northeast Border Region Northwest Border Region Southwest Border 
Region 

 lnPI lnPI lnPI 
lnASCF 0.2333 0.3807** 0.3929*** 

 (1.5761) (5.2271) (53.9365) 
indu 0.1122 0.5034 0.5124*** 

 (1.8852) (2.0823) (29.0531) 
fdi -5.3017* -23.0056*** -6.5658* 

 (-3.4320) (-10.5388) (-3.6926) 
gov 0.9232 -1.5972 -0.6062** 

 (0.8309) (-1.4015) (-8.3473) 
lnmac 0.5780 0.3090* -0.1449 

 (1.3157) (3.2204) (-0.5241) 
_cons 2.4013 3.5213* 6.8486* 

 (1.1561) (2.9589) (3.2694) 
Individual fixation Yes Yes Yes 

N 33 33 33 
R2 0.9357 0.9678 0.9418 

t statistics in parentheses 
* p < 0.1, ** p < 0.05, *** p < 0.01 

 

5. Conclusion and Recommendations 
This article employs panel data from nine provinces and 

regions in China's border regions from 2011 to 2021, utilising 
fixed effects and intermediary effects models to empirically 
test the poverty reduction effect and transmission mechanism 
of agricultural supply chain finance. The following 
conclusions are drawn: 

One is that agricultural supply chain finance has a direct 
impact on the increase of per capita disposable income in rural 
areas. Secondly, it can be posited that the transmission of 
agricultural supply chain finance is facilitated by the 
development of digital finance and the improvement of the 
urbanisation rate, thereby increasing the per capita disposable 
income of rural areas in various provinces and regions. 
Thirdly, based on the heterogeneity regression results of the 
nine provinces and regions in China's border areas, it can be 
concluded that the development of agricultural supply chain 
finance has a significant effect on the increase of per capita 
disposable income in rural areas. There is heterogeneity in the 
effect on different regions, with the most significant effect 
observed in the southwest border area, followed by the 
northwest border area, and finally the northeast border area. 

This article will present the following policy 

recommendations: 
One is to continue to promote the development of 

agricultural supply chain finance. Based on the analysis of the 
development level of agricultural supply chain finance in 
China's nine border provinces and regions, it can be found that 
there is still a significant gap between different regions. 
Therefore, it is necessary to persist in promoting the deep 
integration of supply chain and financial services, and 
establish a win-win agricultural supply chain financial service 
system. This measure helps to narrow the development gap 
between regions, promote balanced development of 
agricultural financial services nationwide, and provide more 
precise and efficient financial support for farmers and 
agriculture in different regions. Furthermore, it is possible to 
establish a cross-departmental and cross-regional 
coordination mechanism for the development of agricultural 
supply chain finance, promote the integration and sharing of 
resources among all parties, and achieve maximum policy 
effectiveness. 

Secondly, it is of paramount importance to fully utilise the 
synergistic effect of agricultural supply chain finance and 
other factors to provide greater assistance for rural 
development and revitalisation. With the rapid development 
of digital finance, financial institutions can be guided to 



 

387 

innovate their agricultural supply chain financing business, 
further breaking traditional models and innovating the 
development model of supply chain finance. Concurrently, 
the government can provide support and regulation for 
agricultural product e-commerce platforms, which can be 
used to optimise the agricultural supply chain. This will result 
in a reduction of intermediate link costs, an increase in 
agricultural product sales prices, and an increase in farmers' 
income. 

Thirdly, it is of paramount importance to reinforce the 
construction of the credit system, enhance risk prevention 
mechanisms, and establish a credit system for the agricultural 
supply chain. By integrating credit data from upstream and 
downstream enterprises in the supply chain into the credit 
system, we can achieve online information sharing and real-
time queries throughout the entire chain. 

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