Digital Economy and Rural Revitalization:
Evidence from China Based on County-level Panel Data

Sho Komatsu
Asian Growth Research InstituteWorking Paper Series Vol. 2024-08
March 2024

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Asian Growth Research InstituteDigital Economy and Rural Revitalization:
Evidence from China Based on County-level Panel Data
Sho Komatsu
Asian Growth Research Institute

Abstract
Digital economy is a new driving force for China’s economic development and thestrategy of rural revitalization is the overall focus of the “three rural issues” in the newera. As the rapid penetration of digital technology into rural areas has a potential tointegrate digital economy with rural revitalization, in-depth empirical research is needed
to find out how digital economy drives China’s rural revitalization. However, relevant
studies mostly focus on the impact of digital economy on one dimension of rural
revitalization and lack a systematic exploration of the overall impact of digital economy
on rural revitalization. To fill the gap with previous studies, this study constructs countylevel comprehensive index system by the entropy weighting method and examines thedriving effect and mechanisms of digital economy on rural revitalization based on several
digital economy indicators and rural revitalization score by using county-level panel data
in Zhejiang during 2010–2021. This study shows that digital economy measured by rural
e-commerce, digital finance indexes, and the number of households using broadband
Internet has a driving effect on rural revitalization. Moreover, this study finds thattechnological innovation and industrial structure transformation are importantmechanisms of digital economy driving rural revitalization. Therefore, it is recommended
that the local government accelerate digital industrialization and industrial digitalization
process.

Keywords: Digital economy; Rural revitalization; China; Panel data
JEL Classification Codes: C23; O18

1

1.

Introduction

Digital economy has become a new and important driving force for China’s economic
development. Moreover, digital economy in rural areas has become a powerful driving
force for rural reform and development, modernization of agriculture and rural areas, andleapfrogging of rural areas (Zhang & Bai, 2022). The rural revitalization strategy is theoverall focus of the work of the “three rural issues” in the new era. The Outline of the
14th Five-Year Plan proposes “Agricultural and Rural Development and Rural
Revitalization” and “An Initiative to Build a Digital China”. Rural revitalization isessentially a special stage in the long process of rural transformation and development(Guo & Liu, 2021). The rural revitalization strategy is one of the most important andfundamental development strategies of the Chinese government. This strategy completes
the overarching task of addressing the recession in China’s rural areas and achieving
China’s great rejuvenation. This strategy has been regarded by the central government as
an important component of modern Chinese socialist society. As such, the strategy has
become a major long-term sustainable development policy and is given top priority (Fanet al., 2023). By 2020, China has achieved eradication of absolute poverty and entered
the stage of rural revitalization. The rapid extension and penetration of digital technology
into agriculture and rural areas provides an opportunity to promote the integration ofdigital economy and rural revitalization, and in-depth empirical research is needed to findout how the digital economy drives China’s rural revitalization, and whether suchpromotion is effective and what aspects of rural revitalization it promotes (He, Wang, &Wang, 2022).
However, relevant studies mostly focus on the impact mechanism of digital economy
on one dimension of rural revitalization, such as agricultural development, social
governance, farmers’ income and farmers’ entrepreneurship, and lack a systematic
exploration of the overall impact mechanism of digital economy on rural revitalization.
Meanwhile, most of the previous studies are qualitative and lack empirical studies
regarding the driving effect of digital economy on rural revitalization in general (Zhao &Long, 2021). Therefore, it is important to examine the impact of digital economy on rural
revitalization. This study examines the driving effect, the mechanism, and heterogeneity2

of digital economy on rural revitalization based on several digital economy indicatorssuch as e-commerce and digital finance, and rural revitalization score by using the countylevel panel data in Zhejiang during the period of 2011–2021.
This study selects Zhejiang as an ideal case. The reasons for this are as follows. First,
digital economy has become the main engine of Zhejiang’s economy. In 2021, Zhejiang’s
digital economy added value reached 3.57 trillion RMB, ranking fourth in China,accounting for 48.6% of GDP, ranking first in China. Moreover, Alibaba, one of thelargest Internet companies in China was founded in Hangzhou, Zhejiang in 1999.
Zhejiang also has the third highest Digital Financial Inclusion Index of China. Second,
Zhejiang is the only model province for rural revitalization in China. In 2021, the Ministryof Agriculture and Rural Affairs and the provincial government of Zhejiang jointly issued
the “High-quality Creation of Rural Revitalization Demonstration Provinces for the
Promotion of Common Prosperity Action Program for Construction of Demonstration
Areas (2021-2025)”, which became the only ministry-province action program forbuilding a model province for rural revitalization. Therefore, the study of Zhejiang serves
as a reference for rural revitalization in other provinces.
This study makes three major contributions to the literature. First, this study
constructs a county-level comprehensive index system to evaluate rural revitalization in
five dimensions, including industrial prosperity, affluent life, civilized rural style,effective governance, and ecological livability. While the existing literature focuses onprovinces and prefecture-level cities, this study uses county-level data to fulfill the
research gaps. Second, this study uses a variety of digital economy indicators from theperspectives of digital application degree and fundamentals of digital industry
development. Third, this study examines mechanisms and heterogeneity of the effects ofdigital economy on rural revitalization.
The remainder of this paper is organized as follows. Section 2 reviews the existing
literature and proposes the hypotheses. Section 3 provides research hypotheses. Section
4 presents the data and estimation method. Section 5 presents and discusses the empiricalresults. Section 6 further analyzes the mediation mechanism. The final section
summarizes the main conclusions.
3

2.

Literature Review

The inherent mechanism of rural revitalization empowered by digital economy is to drivechanges in the modes of production, living and governance in the rural area with the helpof modern information technology and digitized knowledge. However, most of therelevant research focuses on the impact mechanism of digital economy on certain
dimensions of rural revitalization, such as agricultural development, rural residents’income and entrepreneurship, and lacks a systematic exploration of the overall impact
mechanism of digital economy on rural revitalization. Furthermore, most of the previous
studies are qualitative and lack empirical measurement of the driving effect of the digital
economy on rural revitalization (Zhao & Long, 2021).
2.1 Research on digital economy
Research on digital economy can be described from three aspects: research on the
exploration of the concept of digital economy, the statistical classification andmeasurement of the digital economy, and the empirical studies related to digital economy(He et al., 2022). Various studies find that the digital economy has a positive impact onsociety, the economy, and the environment. More specifically, digital economy can boost
rural income (Sun & Liu, 2022), improve rural residents’ quality of life (Wang et al.,2023), heighten total factor productivity (Tian & Liu, 2021; Wan & Luo, 2022), mitigate
environmental pollution (Zhou & He, 2020; Che & Wang, 2022), reduce carbon emissions(Chen et al., 2022; Yi et al., 2022), optimize industrial structure (Liu & Chen, 2021),
promote high-quality economic development (Ren, 2021), and narrow urban-rural
income gap (Gao & Li, 2023).
2.2 Research on rural revitalization
Research on rural revitalization focuses on three aspects: research on the main body ofrural revitalization, rural revitalization paths, and rural revitalization evaluation index
system (He et al., 2022). Regarding research on rural revitalization evaluation index
system, most scholars start from the five dimensions of industrial prosperity, ecologicallivability, rural civilization, effective governance and affluent life to construct the rural
revitalization evaluation index system (Yan & Wu, 2019; Zhang et al, 2018; Jia et al,
4

2018), and basically a consensus has been reached on the selection of the first-levelindexes (He et al., 2022).
Regarding the empirical studies on rural revitalization, using province-level panel
data from 2011 to 2020, Fan et al. (2023) examine the impacts of financial development
on rural revitalization and find that financial development has significantly promoted
rural revitalization, while the development of urbanization and tourism have significantly
inhibited the promotion of rural revitalization. Using province-level panel data from 2018
to 2020, Zhang, Yang, and Li (2022) examine the impact of fiscal expenditure structure
on rural revitalization and find that while the total amount of fiscal resources related toagriculture has a significant impact on rural revitalization, the expenditure structure of
fiscal resources related to agriculture also affects the effectiveness of rural revitalization.
2.3 Research on the impact of the digital economy on rural revitalization
Although previous studies provide valuable insights into digital economy and rural
economy, few studies have examined the impact of digital economy on rural revitalization.
Regarding direct study on the impact of digital economy on rural revitalization, using
province-level panel data from 2015 to 2019, Zhao and Long (2021) find that digital
economy has a significantly positive effect on rural revitalization, and its effect isheterogeneous among regions. The statistically significant positive effect is found in thecentral and western regions, while the positive effect in the eastern region is insignificant.
Moreover, using province-level panel data from 2011 to 2018, He et al. (2022) find that
digital economy promotes rural revitalization. Technological innovation and humancapital are also found to be important mechanisms for rural revitalization driven by digital
economy. Furthermore, using panel data from 11 prefecture-level cities in Zhejiang from2011 to 2019, Cen, Lin, and Wu (2022) find that digital economy promotes rural
revitalization.
To the best of my knowledge, no study has focused on the impact of digital economy
including e-commerce on rural revitalization using county-level panel data. This study is
unique in that it provides new evidence on digital economy and rural revitalization.

3.

Research hypothesis
53.1 Direct effect of digital economy on rural revitalization
Digital economy such as digital inclusive finance, digital technology, the Internet and ecommerce promotes rural revitalization 1 (He et al., 2022). Digital economy has amultifaceted impact on rural revitalization: first, it brings new development modes to therural areas such as agricultural digitization and cloud tourism; second, it brings efficient
services and convenient life to the rural areas such as improving the efficiency of rural
logistics and distribution; and third, it creates a fairer social environment in the rural areas,
and further realizes information sharing (Feng et al., 2023). Zhao and Long (2021) explain
the effect of digital economy on rural revitalization from the perspectives of industrial
prosperity, ecological and livable, vernacular civilization, effective governance, andliving well. First, digital economy promotes the construction of a modern agricultural
system in rural areas, thereby promoting the prosperity of rural industries. Specifically,
the digital economy promotes the integration of primary, secondary, and tertiary industries
and the construction of a modern agricultural industrial system. Second, digital economy
is helping to promote rural ecological livability through ecological remediation andoptimized allocation of ecological resources. Digital technology enables to create an early
warning system for rural habitat data, track trends in environmental development in realtime, and respond to abnormal changes in a timely manner. Third, digital economy
promotes rural civilization through the preservation and development of outstanding rural
culture. Digital technology provides strong support for the storage, analysis andprediction of rural cultural data. The relevant departments have set up databases on rural
culture, storing traditional culture (such as historical relics and handmade crafts) andhumanities and cultural data of the villages in which they are located in the databases, so
as to realize the inheritance and development of rural culture by means of the wide
dissemination of the Internet. Fourth, digital economy contributes to effective rural
governance by increasing the enthusiasm and effectiveness of villagers' participation inrural governance. Digital technology has broken the original social, economic, relational,

1

He et al. (2022) provide the comprehensive explanations of the effect of digital economy on rural
revitalization from the perspectives of industrial prosperity, ecological and livable, vernacular
civilization, effective governance, and living well.
6geo-structural, and cultural structures of the countryside, forming an interactive group
governance model characterized by interactivity and group structure. Fifth, digital
economy is expanding the channels for farmers to increase their income and realizing an
increase in farmers’ income, thus contributing to the prosperity of rural life. Internet use
and the development of digital inclusive finance can significantly increase the level offarmers’ non-agricultural income. Relying on the Internet and big data, more effective
sales channels can be opened up for farmers, upgrading the efficiency of upstream anddownstream circulation, and driving the downward movement of high-quality agricultural
products. Therefore, this study puts forward the following hypothesis:
H1: Digital economy promotes rural revitalization.
3.2 The indirect impact of digital economy on rural revitalization
Digital economy can also affect rural revitalization indirectly. Big data, the Internet andother digital technologies have broken the barrier between the rural areas and the outside
world, prompting a large-scale flow of factors such as talent, knowledge and technology
to the rural areas, and vigorously promoting the comprehensive revitalization of the rural
areas. The impact mechanism of digital economy on rural revitalization includes thegrowth pole effect, human capital effect, and technological innovation effect (Zhang &Deng, 2023).
Growth pole effect can be explained as follows. The development of digital economy
provides infrastructure protection for regional economic development, promotes the scale
agglomeration of factor resources, and is conducive to the formation of new growth poles
and the realization of regional economic structure restructuring (Zhang, 2019). The
growth poles can generate economies of scale for the development of rural areas, drive
the growth of rural residents’ income through transfer income and other means (Chen &Wei, 2022), and then drive rural revitalization (Zhang & Deng, 2023).
Human capital effect can be explained as follows. With the support ofcommunication infrastructure, farmers can learn the necessary knowledge and basic skills
for modern production at a lower cost, improve their knowledge and technology, andenhance their understanding of new agricultural technologies and advanced production
information (Li & Li, 2022), which can effectively improve the level of human capital in7rural areas, change and optimize farmers’ traditional production and life style, and create
endogenous power for rural revitalization (Zhang & Deng, 2023). Moreover, in 2021, theOpinions on Accelerating the Revitalization of Rural Talents issued by the State Council
and the General Office of the State Council of the People's Republic of China proposedthat the key to rural revitalization lies in people, and that the development of rural humancapital should be given top priority (He et al., 2022).
Technological innovation effect can be explained as follows. Digital economy ischaracterized by real-time transmission of information, which makes knowledge elements
flow at high speed among innovation subjects, promotes the aggregation of innovation
elements, facilitates agriculture-related business subjects to obtain innovation resourcesalong the channel of information network (Zhao, 2021), and applies new innovativetechnologies to transform the traditional production and operation mode, extend theindustrial chain, and enhance the value chain in the rural areas (Wen & Chen, 2020),
promoting rural revitalization (Zhang & Deng, 2023). Additionally, in 2019, the Ministryof Science and Technology proposed that rural revitalization should be driven by
innovation.
Furthermore, digital economy has become an important driving force for industrial
upgrading, because industrial structure layout can be optimized by digital technologies(Zhang, 2018). Digital infrastructures include mobile Internet, big data, cloud computing,and artificial intelligence. These technologies can improve the traditional industrial
structure, supporting the upgrading and transformation of the industrial structure. Digital
economy will promote the upgrading, production efficiency, and high-quality
development of traditional industries (Sheng, 2020).
Due to the limitations of data, this study examines the mechanism of digital economy
on rural revitalization from the dimensions of technological innovation and industrial
upgrading. Therefore, this study puts forward the following hypotheses:
H2: Digital economy promotes rural revitalization through technological innovation.
H3: Digital economy promotes rural revitalization through industrial upgrading.

4.

Methodology
84.1 Data
To test the impact of digital economy on rural revitalization, this study constructed thecounty-level panel data in Zhejiang from 2010 to 2021 mainly based on Zhejiang
Statistical Yearbooks and China Statistical Yearbook (county-level). Regarding digital
economy data, Taobao Village list published by Alibaba, list of e-commerce
demonstration villages and rural e-commerce demonstration service stations from
Department of Commerce of Zhejiang, The Peking University Digital Financial Inclusion
Index of China from the Institute of Digital Finance at Peking University and Ant Group.
4.2 Selection of variables
Dependent variable is rural revitalization score. With reference to existing studies (Cenet al., 2022; Fan et al., 2023; Liu & Yang, 2020; Wang, 2023), this study constructs acomprehensive score of five dimensions: prosperous industry, affluent life, civilized ruralstyle, effective governance, and ecological livability, and uses the objective weighting
method, specifically the entropy weight method for the calculation. Due to the limitationof data, this study uses 13 measurements from the above five dimensions for the
calculation of rural revitalization score. These measurements include proportion of added
value of primary industry to GDP, grain per capita, total power of agricultural machinery,
fertilizer use per 10,000 people, urban-rural income gap, effective irrigation area, and per
capita net income of rural residents. More specifically, Table 1 shows the specificevaluation score of rural revitalization.
Independent variable is digital economy. Although there is no consensus on themeasurement of digitalization (Lee et al., 2022), e-commerce constitutes a considerable
part of the digital economy (Kong, 2019). This study evaluates digital economy using
several indicators such as rural e-commerce, digitization level, digital finance index, thenumber of households using mobile phones, and the number of households using
broadband Internet. As Taobao Villages demonstrate the remarkable progress of ecommerce in rural China (Qin & Fang, 2022) and referring to the existing studies which
use rural e-commerce demonstration county policy as a quasi-natural experiment (Penget al., 2021; Qin & Fang, 2022; Qin et al., 2023), rural e-commerce is measured by the
number of Taobao Villages and e-commerce demonstration villages and the proportion of
9Taobao Villages and e-commerce demonstration villages in the county. AliResearch(Alibaba Group’s research unit)’s identification criteria for a Taobao village include: (1)trading place: operation place is located in rural area with administrative village as unit;(2) trading volume: annual trading volume of e-commerce amounts to over RMB10
million; and (3) scale of online merchants: active online stores amount to over 100 or
account for over 10 percent of local households. Moreover, following Li, Wang, Zhao
(2021), this study uses the number of Taobao Towns as another indicator of e-commerce.
According to Alibaba’s official definition, the economic and social value of Taobao
Towns is slightly different from that of Taobao Villages, with Taobao Towns having amore significant scale effect and emphasizing the degree of agglomeration of Taobao
Villages (Li et al., 2021). Taobao Town is defined as a town or sub-district with three
Taobao villages or above (AliResearch, 2016).
Regarding control variables, referring to existing studies (Fan et al., 2023; Zhao &Long, 2021) and data unavailability, this study uses the level of economic development
measured by the logarithm of per capita GDP, trade openness measured by the proportionof total import and export to GDP, the level of financial development measured by the
proportion of total loans of financial institutions to GDP, and the degree of government
intervention measured by the proportion of government fiscal expenditure to GDP. Table
2 shows the descriptive statistics of each variable.

10Table 1 evaluation score of rural revitalization
Sign
Primary Measurement

Secondary Measurement
Prediction
Proportion of added value of primary industry in regional GDP (%)

Negative

Agricultural labor productivity (the gross output value of agriculture, forestry, animal
Positivehusbandry and fishery per capita (RMB))
Industrial prosperity
Grain per capita (kg)

Positive

Total power of agricultural machinery (10000 kW)

Positive

Electricity consumption per capita in rural areas

Positive

Number of beds in health institutions per 10,000 people

Positive

Length of highway mileage per square kilometer (km)Positive

Number of doctors per 10,000 people (people)

Positive

Fertilizer use per 10,000 people (tons)

Positive

Education (regular secondary school student enrollment per 10,000 people)

Positive

Urban-rural income gap (times)

Negative

Effective irrigation area (1000 ha)

Positive

Actual per capita net income of rural residents (RMB)

Positive

Ecological and livable

Vernacular civilization

Effective governance

Living well

Table 2 Descriptive statistics
VariableObsrural revitalization score

624

0.620

0.102

0.341

0.868

Proportion of Taobao Villages

624

3.172

8.188

0.000

69.655

Number of Taobao Villages

624

1.069

1.411

0.000

5.288

Number of Taobao Towns

624

0.455

0.765

0.000

2.773

Proportion of e-commerce demonstration villages

624

0.411

1.314

0.000

10.417

Number of e-commerce demonstration villages

624

0.288

0.690

0.000

3.829

Digital financial digitization index

414

87.65437.4541.040

136.448

11

Mean

Std. dev.

Min

Max

The Peking University Digital Financial Inclusion Index

414

112.123

16.36962.860136.562

Number of mobile phone users

621

4.013

0.789

2.163

5.846

Number of broadband users

617

2.531

0.897

-0.045

4.595

Telecom Business Income

612

10.708

0.905

8.648

12.587

real GDP per capita

624

10.824

0.469

9.288

12.209share of secondary production in GDP

624

47.8569.650

13.63874.099share of tertiary production in GDP

624

44.2358.168

19.19169.993

retail sales of consumer goods per capita

624

10.065

0.500

8.468

11.209loans in financial institutions/GDP

624

126.859

34.02136.222289.518

proportion of fiscal expenditure to GDP

624

18.173

11.4235.854

71.335proportion of total import and export trade to GDP

622

12.98921.1450.289

225.589

Number of beds in health institutions

624

35.481

11.1989.849

68.784

Number of industrial enterprises

624

5.660

1.158

2.197

7.628

patent application approved per capita

624

3.178

0.886

0.154

5.569

industrial structure upgrading

624

236.3519.291

212.480268.393

4.3 Measurement of rural revitalization level and digital economy level
In the selection of specific evaluation methods, this study selects the entropy weight
method as the results calculated by the entropy weight method are more accurate andmore objective, which can provide a better explanation for the analyzed results (Fan etal., 2023).
From county-level perspective, the evaluation indicators for the rural revitalizationlevel and the digital economy development level are multidimensional andcomprehensive evaluation indicator systems, and the smaller the number of indicators,
the more suitable the entropy weighting method with higher accuracy (Fan et al., 2023).
Referring to the existing studies (Fan et al., 2023; Zhao & Long, 2021), the specificprocedure is as follows.
Step 1: Standardization of Indicators
As the units of measurement for each indicator are not uniform, the indicators must firstbe standardized. To avoid meaningless logarithmic calculations when calculating entropy
12values, positive and negative indicators must be non-negative in the calculation and musthave a uniform addition of 0.0001.
′
Positive indicators: 𝑋𝑋𝑖𝑖𝑖𝑖= �max�𝑋𝑋′
Negative indicators: 𝑋𝑋𝑖𝑖𝑖𝑖=�max (𝑋𝑋1𝑗𝑗, , 𝑋𝑋2𝑗𝑗, ,… ,𝑋𝑋𝑛𝑛𝑛𝑛, )− 𝑋𝑋𝑖𝑖𝑖𝑖

1𝑗𝑗, , 𝑋𝑋2𝑗𝑗, ,… ,𝑋𝑋𝑛𝑛𝑛𝑛, �−min(𝑋𝑋1𝑗𝑗, , 𝑋𝑋2𝑗𝑗, ,… ,𝑋𝑋𝑛𝑛𝑛𝑛, )𝑋𝑋𝑖𝑖𝑖𝑖 −min (𝑋𝑋1𝑗𝑗, , 𝑋𝑋2𝑗𝑗, ,… ,𝑋𝑋𝑛𝑛𝑛𝑛, )� + 0.0001
� + 0.0001

max�𝑋𝑋1𝑗𝑗, , 𝑋𝑋2𝑗𝑗, ,… ,𝑋𝑋𝑛𝑛𝑛𝑛, �−min(𝑋𝑋1𝑗𝑗, , 𝑋𝑋2𝑗𝑗, ,… ,𝑋𝑋𝑛𝑛𝑛𝑛, )Step 2: Calculation of the percentage of county i in index j.
′��𝑃𝑃𝑖𝑖𝑖𝑖 = 𝑋𝑋𝑖𝑖𝑖𝑖

𝑛𝑛

𝑖𝑖=1

′
𝑋𝑋𝑖𝑖𝑖𝑖

Step 3: Calculation of the entropy value of each index.
𝐸𝐸𝑗𝑗 = − ln (𝑛𝑛) ∑𝑛𝑛

1, 0 ≤ 𝐸𝐸𝑗𝑗 ≤ 1

𝑖𝑖=1 𝑃𝑃𝑖𝑖𝑖𝑖 ln (𝑃𝑃𝑖𝑖𝑖𝑖 )Step 4: Calculation of the difference coefficient of the indicator j in all indicators.
𝐺𝐺𝑗𝑗 = 1 − 𝐸𝐸𝑗𝑗 , 0 ≤ 𝐺𝐺𝑗𝑗 ≤ 1

Step 5: Calculation of the weight of the indicator j in all indicators.
𝑤𝑤𝑗𝑗 = 𝐺𝐺𝑗𝑗 ��

𝑚𝑚

𝑗𝑗=1

𝐺𝐺𝑗𝑗

Step 6: Calculation of the comprehensive scores of each county.

4.4 Model

𝑠𝑠𝑗𝑗 = �

𝑛𝑛

𝑖𝑖=1

′
𝑤𝑤𝑗𝑗 𝑋𝑋𝑖𝑖𝑖𝑖

This study adopts fixed effects (FE) model. Regarding e-commerce in digital economy,
this study takes the emergence of Taobao Villages as an exogenous shock and take theproportion of Taobao Villages in the administrative villages of the county as the policy
shock variable. The following empirical model is established to examine the effect ofdigital economy on rural revitalization:
𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅_𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖 = 𝛼𝛼0 +𝛼𝛼1 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷_𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑖𝑖𝑖𝑖 +δ𝑋𝑋𝑖𝑖𝑖𝑖 +𝜆𝜆𝑖𝑖 +𝐶𝐶𝑐𝑐𝑐𝑐 +𝜀𝜀𝑖𝑖𝑖𝑖(1)

𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅_𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖 represents the level of rural revitalization and development in
the county i and the year t; 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷_𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑖𝑖𝑖𝑖 represents digital economy
development level of the county i and the year t, which is the core independent variable;
13

𝑋𝑋𝑖𝑖𝑖𝑖 represents a series of control variables; 𝜆𝜆𝑖𝑖 represents a county-fixed effect; 𝐶𝐶𝑐𝑐𝑐𝑐
represents city-by-year effect; 𝜀𝜀𝑖𝑖𝑖𝑖 represents a random disturbance term.

This study also uses a combination model of the fixed effects and two-stage leastsquares (FE_2SLS) for robustness checks. This study selects digital economy variablesof prefecture-level cities and a historical variable as instrumental variables. Regarding
digital economy variables of prefecture-level cities, number of Taobao Villages and ecommerce demonstration villages in the cities to which the county belongs are used. The
rationale for this is as follows. Digital economy variables of prefecture-level cities towhich the county belongs can attract e-commerce development in the county. Thus, thisvariable meets a criterion of the instrumental variable—“relevance.” Regarding anothercriterion of IVs—“exclusion restriction”—this IV does not seem to affect rural
revitalization in the counties directly except through digital economy development in the
counties. Regarding historical variables, the number of telephones in 1989 of prefecturelevel cities to which a certain county belongs is used. The validity of this IV can be
explained as follows. Digital economy has been empowered by the development ofInternet technology. As suggested by the evolution of Internet access technology in China,
Internet technology should have originated from the spread of landline phones. Before
that, people communicated mainly through the post office system, the department
responsible for laying landline phones. Therefore, areas with a historically high numberof post offices and landline phones are likely to have a high-level digital economy,
meeting the relevance requirement. Nonetheless, the historical impact of the number ofpost offices and landline phones on regional economies is fading. Nowadays, the numberof post offices and landline phones has little effect on rural revitalization, meeting theexclusion restriction.

5.

Empirical results

5.1 Impact of digital economy on rural revitalization
Table 3 presents the benchmark results estimated using FE model. Columns (1) - (5) show
the effect of e-commerce on rural revitalization. Column (1) and (2) are the estimatedresult of the effect of Taobao Village on rural revitalization. Column (3) is the estimated14

result of the effect of Taobao Town on rural revitalization. Column (4) and (5) is theestimated result of the effect of e-commerce demonstration villages on rural revitalization.
Column (6) and (7) are the estimated result of digital financial digitization index and thePeking University Digital Financial Inclusion Index on rural revitalization, respectively.
Column (8) - (10) are the estimated result of the effect of the scale of mobile phone users,
broadband Internet users, and telecom business income on rural revitalization,
respectively. Table 3 shows that most of the digital economy variables have a significant
positive effect on rural revitalization. In particular, rural e-commerce measured by Taobao
Village/Town and digital finance measured by digital financial digitization index and thePeking University Digital Financial Inclusion Index promote rural revitalization.
5.2 Endogeneity test
When identifying the effect of digital economy on rural revitalization, there may beinterference due to endogeneity problems such as missing variables and mutual causality.
To address the potential endogeneity, the instrumental variable method is used. Table 4
presents the estimation results of FE_2SLS. The Columns in Table 4 correspond to thebenchmark regression one by one. In the first stage estimation, IVs have a significant
positive impact on digital economy variables. Besides, the first stage passed the overidentification test and the test of weak instrumental variables. Kleibergen-Paap rk WaldF statistic is almost always above 10, the critical value proposed by Stock and Yogo
(2005), indicating that the instrumental variables are effective. There are no majordifferences from the estimation results of benchmark regression. This indicates that theestimation results of instrumental variables are robust and after overcoming theendogeneity problem, the effect of digital economy on rural revitalization remains stable.
More specifically, rural e-commerce, digitization level, Digital Financial Inclusion
Indexes, and the number of households using broadband Internet significantly andpositively affect rural revitalization score.

15

Table 3 Benchmark results estimated using fixed effects (FE) model
(1)

(2)

(3)

VARIABLES

Proportion of Taobao Villages

(4)

(5)

(6)

(7)

Rural revitalization score

0.002***(0.000)

Number of Taobao Villages

0.014***(0.004)

Number of Taobao Towns

0.022***(0.006)

Proportion of e-commerce demonstration villages

0.005**(0.002)

Number of e-commerce demonstration villages

0.007(0.005)

Digital financial digitization index

0.001***(0.000)

The Peking University Digital Financial Inclusion Index

0.001***(0.000)

16

(8)

(9)

(10)

Number of mobile phone users

0.088***(0.019)Number of broadband users

0.044**(0.017)

Telecom Business Income-0.023(0.016)

Controls

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

County-fixed effect

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

city-by-year effect

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Observations

622

622

622

622

622

412

412

619

615

610

R-squared

0.546

0.570

0.559

0.524

0.518

0.680

0.636

0.548

0.549

0.516

Number of county_id

52

52

52

52

52

52

52

52

52

52

Within R-squared

0.546

0.570

0.559

0.524

0.518

0.680

0.636

0.548

0.549

0.516

Between R-squared

0.342

0.384

0.370

0.296

0.304

0.429

0.398

0.005

0.099

0.423

Overall R-squared

0.380

0.419

0.405

0.338

0.343

0.480

0.444

0.037

0.163

0.444

Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

17

Table 4 Results of the fixed effects and two-stage least squares (FE_2SLS)
(1)

(2)

(3)

(4)

(5)

(6)

(7)

Second stage
VARIABLES

Proportion of Taobao Villages

Rural revitalization score0.004***(0.001)

Number of Taobao Villages

0.026***(0.006)

Number of Taobao Towns

0.040***(0.010)

Proportion of e-commerce demonstration villages

0.014***(0.004)

Number of e-commerce demonstration villages

0.027***(0.008)

Digital financial digitization index

0.001***(0.000)

The Peking University Digital Financial Inclusion Index

0.002***(0.000)

18

(8)

(9)

(10)

Number of mobile phone users

0.307***(0.071)

Number of broadband users

0.173***(0.046)

Telecom Business Income

0.031(0.026)

Controls

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

County-fixed effect

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

City-by-year effect

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Observations

622

622

622

622

622

412

412

618

613

609

Number of county_id

52

52

52

52

52

52

52

52

52

52

Within R-squared

0.500

0.529

0.529

0.494

0.478

0.677

0.613

0.332

0.235

0.493

Between R-squared

0.383

0.441

0.418

0.291

0.318

0.426

0.385

0.103

0.020

0.106

Overall R-squared

0.408

0.460

0.440

0.330

0.349

0.477

0.432

0.043

0.000

0.160

97.174***13.503***8.844***

88.319***44.648***0.459***

0.308***

0.050***

0.046***

0.112***First stage
City-level digital economy variable

Number of telephones in 1989 of cities×current year’s
digital economy variables

19

First-stage F-statistic

9.6

16.19

25.67

16.19

17.69

276.77778.82

55.84

228.9

66.09

Cragg-Donald Wald F statistic

287.427209.397

310.741187.437210.537455.72366.055

52.06443.758177.822

Kleibergen-Paap Wald rk F statistic

13.54741.86852.27319.26430.952234.478720.21434.5937.505

136.882

Chi-sq(1)Chi-sq(1)Chi-sq(1)Chi-sq(1)Chi-sq(1)Chi-sq(1)Chi-sq(1)Chi-sq(1)Chi-sq(1)Chi-sq(1)=25.67***=25.67***=25.67***=12.87***=12.87***=52.95***=24.95***=17.32***=11.93=1.54

Anderson-Rubin Wald test

Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

20

5.3. Heterogeneity Analysis
The subject of this research is the counties or county-level cities of Zhejiang. In China’s
administrative divisions, counties and county-level cities have certain differences in fiscal
self-management

capacity,

economicdevelopment

autonomy,andeconomicdevelopment level. It is problematic to analyze them indiscriminately (Li & Qin, 2022).
Therefore, it is necessary to examine counties and county-level cities separately. Table 5
and Table 6 show the estimated results of FE_2SLS for counties and county-level cities,
respectively. The results are mostly consistent with those of benchmark regression model
for both counties and county-level cities. This implies that the promotion effect of digital
economy on rural revitalization is not significantly different from counties and countylevel cities.
Moreover, given the considerable differences in the level of economic development
and the geographic environment of northeastern Zhejiang and southwestern Zhejiang, thisstudy examines the heterogeneity of the two regions. Table 7 and Table 8 show theestimated results of FE_2SLS for northeastern Zhejiang and southwestern Zhejiang,
respectively. The results show that most of the digital economy indicators have significant
positive effects on rural revitalization in both northeastern and southwestern Zhejiang.
The results also show some differences between these two regions. In particular, thenumber of mobile phone users and that of broadband users have a significantly positiveeffect on rural revitalization for southwestern Zhejiang, but not for northeastern Zhejiang.
Additionally, both proportion and number of rural e-commerce demonstration villages
have a significantly positive effect on rural revitalization for northeastern Zhejiang, but
not for southwestern Zhejiang. The overall results are mostly consistent with those ofbenchmark regression model. Additionally, the coefficients of rural e-commerceindicators are larger in northeastern Zhejiang than southwestern Zhejiang. This implies
that the promotion effect on rural revitalization in underdeveloped southwestern Zhejiang
is weaker than that in developed northeastern Zhejiang.

21

Table 5 Results of the fixed effects and two-stage least squares (FE_2SLS) for counties
(1)

(2)

(3)

(4)

VARIABLES

Proportion of Taobao Villages

(5)

(6)

(7)

Rural revitalization score

0.007**(0.003)

Number of Taobao Villages

0.027**(0.011)

Number of Taobao Towns

0.038**(0.016)

Proportion of e-commerce demonstration villages

0.017***(0.006)

Number of e-commerce demonstration villages

0.028**(0.011)

Digital financial digitization index

0.001***(0.000)

The Peking University Digital Financial Inclusion Index

0.002***(0.001)

22

(8)

(9)

(10)

Number of mobile phone users

0.361***(0.085)

Number of broadband users

0.275***(0.076)

Telecom Business Income

0.034(0.044)Controls

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

County-fixed effect

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

City-by-year effect

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Observations

394

394

394

394

394

262

262

390

389

382

Number of county_id

33

33

33

33

33

33

33

33

33

33

Within R-squared

0.540

0.512

0.521

0.536

0.535

0.660

0.611

0.315

Between R-squared

0.351

0.380

0.330

0.303

0.340

0.526

0.481

0.016

0.001

0.179

Overall R-squared

0.386

0.403

0.362

0.353

0.381

0.549

0.502

0.000

0.008

0.236

39.216***10.051***

7.043***

61.473***37.526***

0.385***

0.314***

0.054***

0.050***

0.095***

0.539

First stage
City-level digital economy variable

Number of telephones in 1989 of cities×current year’s
digital economy variables

23

First-stage F-statistic

8.47

9.21

13.82

8.79

10.38

184.45585.6530.79

288.05102.38Cragg-Donald Wald F statistic

106.955

83.288172.42778.04899.348161.506

247.536

32.46128.42994.503

Kleibergen-Paap Wald rk F statistic

19.05221.49129.88313.51119.68266.196333.164

18.75612.172103.444

Chi-sq(1)Chi-sq(1)Chi-sq(1)Chi-sq(1)Chi-sq(1)Chi-sq(1)Chi-sq(1)Chi-sq(1)Chi-sq(1)Chi-sq(1)=8.84***=8.84***=8.84***=5.57***=5.57***=34.61***=24.95***=13.82***=33.34

=0.64

(9)

(10)

Anderson-Rubin Wald test

Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1
Table 6 Results of the fixed effects and two-stage least squares (FE_2SLS) for county-level cities
(1)

(2)

(3)

(4)

VARIABLES

Proportion of Taobao Villages

0.003***

0.031***(0.009)Number of Taobao Towns(6)

Rural revitalization score(0.001)
Number of Taobao Villages(5)0.051***(0.016)

Proportion of e-commerce demonstration villages

0.009**

24

(7)

(8)

(0.005)
Number of e-commerce demonstration villages

0.021**(0.010)Digital financial digitization index

0.000***(0.000)

The Peking University Digital Financial Inclusion Index

0.001***(0.000)

Number of mobile phone users

0.147(0.096)

Number of broadband users

0.046(0.057)

Telecom Business Income

0.004(0.035)Controls

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

County-fixed effect

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

City-by-year effect

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Observations

228

228

228

228

228

150

150

228

224

227

Number of county_id

19

19

19

19

19

19

19

19

19

19

Within R-squared

0.553

0.575

0.561

0.522

0.481

0.773

0.742

0.497

0.525

0.531

25

Between R-squared

0.301

0.405

0.370

0.238

0.248

0.041

0.022

0.000

0.085

0.191

Overall R-squared

0.372

0.456

0.428

0.313

0.309

0.183

0.134

0.025

0.171

0.279

158.103***

14.039***8.619***

137.059***61.015***0.494***

0.322***

0.045***

0.037

0.135***First stage
City-level digital economy variable

Number of telephones in 1989 of cities×current year’s
digital economy variables

First-stage F-statistic

32.01

47.40

126.35

36.34

93.31

818.50606.41

63.04

112.44

91.21

Cragg-Donald Wald F statistic

187.21371.99581.638140.174

146.108222.9578.18916.2689.581

77.545

Kleibergen-Paap Wald rk F statistic

17.41

42.60134.02614.34321.789159.414130.162

15.9622.281

105.058

Chi-sq(1)Chi-sq(1)Chi-sq(1)Chi-sq(1)Chi-sq(1)Chi-sq(1)Chi-sq(1)Chi-sq(1)Chi-sq(1)Chi-sq(1)=20.22***=20.22***=20.22***=7.19***=7.19***=16.00***=9.89***=2.74*

=0.45

=0.01

Anderson-Rubin Wald test

Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

26

Table 7 Results of the fixed effects and two-stage least squares (FE_2SLS) for northeastern Zhejiang
(1)

(2)

(3)

(4)

VARIABLES

Proportion of Taobao Villages

(5)

(6)

(7)

Rural revitalization score

0.002**(0.001)

Number of Taobao Villages

0.041***(0.016)

Number of Taobao Towns

0.056**(0.022)

Proportion of e-commerce demonstration villages

0.011***(0.004)

Number of e-commerce demonstration villages

0.027***(0.010)Digital financial digitization index

0.001***(0.000)

The Peking University Digital Financial Inclusion Index

0.002***(0.000)

27

(8)

(9)

(10)

Number of mobile phone users-0.079(0.149)Number of broadband users-0.019(0.107)

Telecom Business Income-0.061*(0.035)Controls

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

County-fixed effect

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

City-by-year effect

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Observations

240

240

240

240

240

158

158

236

231

231

Number of county_id

20

20

20

20

20

20

20

20

20

20

Within R-squared

0.704

0.463

0.561

0.709

0.684

0.811

0.729

0.669

0.661

0.709

Between R-squared

0.009

0.250

0.003

0.046

0.024

0.006

0.074

0.182

0.004

0.319

Overall R-squared

0.131

0.280

0.104

0.019

0.029

0.165

0.015

0.298

0.061

0.452

107.388**5.798***

4.323***

109.374***

45.049***0.479***

0.290***

0.151**

0.027

0.113***First stage
City-level digital economy variable

Number of telephones in 1989 of cities×current year’s
digital economy variables

28

First-stage F-statistic

7.95

11.79

33.90

32.08

59.48

19.93

1900.22148.97

31.2

30.16

Cragg-Donald Wald F statistic

88.54618.04328.80397.891122.009167.041

93.278

2.63

4.323

46.843

Kleibergen-Paap Wald rk F statistic

6.938

7.143

11.43

23.45239.382145.361409.796

5.329

1.181

27.653Chi-sq(1)Chi-sq(1)Chi-sq(1)Chi-sq(1)Chi-sq(1)Chi-sq(1)Chi-sq(1)Chi-sq(1)Chi-sq(1)Chi-sq(1)=12.77***=12.77***=12.77***=11.28***=11.28***=19.93***=17.02***

=0.34

=0.05

=4.07**(9)(10)

Anderson-Rubin Wald test

Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1
Table 8 Results of the fixed effects and two-stage least squares (FE_2SLS) for southwestern Zhejiang
(1)

(2)

(3)

(4)

VARIABLES

Proportion of Taobao Villages

0.004**

0.015***(0.005)

Number of Taobao Towns(6)

Rural revitalization score(0.002)
Number of Taobao Villages(5)0.023***(0.009)Proportion of e-commerce demonstration villages

0.007

29

(7)

(8)

(0.005)
Number of e-commerce demonstration villages

0.011(0.007)

Digital financial digitization index

0.001***(0.000)

The Peking University Digital Financial Inclusion Index

0.001***(0.000)

Number of mobile phone users

0.248***(0.064)

Number of broadband users

0.215**(0.084)

Telecom Business Income

0.049(0.036)

Controls

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

County-fixed effect

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

City-by-year effect

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Observations

382

382

382

382

382

254

254

382

382

378

Number of county_id

32

32

32

32

32

32

32

32

32

32

Within R-squared

0.576

0.627

0.621

0.585

0.592

0.655

0.638

0.549

0.206

0.590

30

Between R-squared

0.319

0.320

0.275

0.249

0.231

0.247

0.246

0.144

0.076

0.004

Overall R-squared

0.388

0.406

0.369

0.350

0.336

0.324

0.324

0.027

0.005

0.089

78.529***18.689***12.278***65.243***43.694***0.371***

0.316***

0.088**

0.045

0.110***First stage
City-level digital economy variable

Number of telephones in 1989 of cities×current year’s
digital economy variables

First-stage F-statistic

39.87

21.76

30.84

14.25

35.16

260.14716.9356.91

537.35

128.66

Cragg-Donald Wald F statistic

144.762

182.43295.20964.277100.624

146.269

131.463

71.92226.252133.757

Kleibergen-Paap Wald rk F statistic18.50749.37355.051

6.327

13.737114.259358.56153.3132.241

182.852

Chi-sq(1)Chi-sq(1)Chi-sq(1)Chi-sq(1)Chi-sq(1)Chi-sq(1)Chi-sq(1)Chi-sq(1)Chi-sq(1)Chi-sq(1)=10.74***=10.74***=10.74***=3.06*=3.06*=23.30***=9.21***=13.65***=6.23**=1.74

Anderson-Rubin Wald test

Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

31

5.4 Robustness checks (Elimination of the impact of COVID-19 epidemic)
Due to the difficulty of excluding unpredictable perturbations in relevant variables
affected by COVID-19 epidemic, referring to Han et al. (2023) and Feng et al. (2023),
this study limits the analysis period during 2010-2019 to remove the impact of COVID19 in 2020 and 2021.
The results are presented in Table 9 and are consistent with those of benchmark
regression model. Therefore, the results are robust to eliminating the particular effect of
COVID-19 in 2020 and 2021.

32

Table 9 Results of the fixed effects and two-stage least squares (FE_2SLS) before COVID-19 epidemic
(1)

(2)

(3)

(4)

VARIABLES

Proportion of Taobao Villages

(5)

(6)

(7)

Rural revitalization score

0.005***(0.001)

Number of Taobao Villages

0.032***(0.007)

Number of Taobao Towns

0.046***(0.010)

Proportion of e-commerce demonstration villages

0.017**(0.007)

Number of e-commerce demonstration villages

0.026**(0.012)

Digital financial digitization index

0.001***(0.000)

The Peking University Digital Financial Inclusion Index

0.002***(0.000)

33

(8)

(9)

(10)

Number of mobile phone users

0.258***(0.085)

Number of broadband users

0.177***(0.057)

Telecom Business Income

0.041(0.025)Controls

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

County-fixed effect

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

City-by-year effect

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Observations

518

518

518

518

518

308

308

518

514

513

Number of county_id

52

52

52

52

52

52

52

52

52

52

Within R-squared

0.565

0.491

0.553

0.546

0.552

0.806

0.642

0.410

0.193

0.535

Between R-squared

0.290

0.472

0.378

0.200

0.212

0.446

0.326

0.064

0.013

0.022

Overall R-squared

0.335

0.477

0.407

0.247

0.258

0.500

0.378

0.023

0.000

0.058

96.597***13.759***9.674***

68.579***44.165***0.380***

0.367***

0.045***

0.04**

0.117***First stage
City-level digital economy variable

Number of telephones in 1989 of cities×current year’s
digital economy variables

34

First-stage F-statistic

6.40

16.11

23.20

10.43

13.24

250.97621.9652.13

167.4985.52

Cragg-Donald Wald F statistic

235.573111.35214.28899.282194.921216.838

252.332

35.11725.472153.053

Kleibergen-Paap Wald rk F statistic

15.11143.192

50.82

10.76

23.721102.319520.043

19.041

4.787

319.633

Chi-sq(1)Chi-sq(1)Chi-sq(1)Chi-sq(1)Chi-sq(1)Chi-sq(1)Chi-sq(1)Chi-sq(1)Chi-sq(1)Chi-sq(1)=30.37***=30.37***=30.37***=4.96**=4.96**=48.62***=72.93***=9.16***=10.40***=2.73*

Anderson-Rubin Wald test

Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

35

6.

Mechanism analysis

Moreover, this study uses mediation effect model to reveal the process and theintermediate mechanism of the influence of the independent variable has on the dependent
variable referring to the study of the mediating effect model (Baron & Kenny, 1986; Heet al., 2022; Wen & Ye, 2014). The following empirical model is established to examine
the role mechanism of rural revitalization by digital economy:
𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅_𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖 = 𝛼𝛼0 +𝛼𝛼1 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷_𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑖𝑖𝑖𝑖 +δ𝑋𝑋𝑖𝑖𝑖𝑖 +𝜆𝜆𝑖𝑖 +𝐶𝐶𝑐𝑐𝑐𝑐 +𝜀𝜀𝑖𝑖𝑖𝑖 (2)𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖 = 𝛽𝛽0 + 𝛽𝛽1 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷_𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑖𝑖𝑖𝑖 +δ𝑋𝑋𝑖𝑖𝑖𝑖 +𝜆𝜆𝑖𝑖 +𝐶𝐶𝑐𝑐𝑐𝑐 +𝜀𝜀𝑖𝑖𝑖𝑖 (3)𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅_𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖 = 𝛾𝛾0 + 𝛾𝛾1 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷_𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑖𝑖𝑖𝑖 + 𝛾𝛾2 𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖 +δ 𝑋𝑋𝑖𝑖𝑖𝑖 + 𝜆𝜆𝑖𝑖 +𝐶𝐶𝑐𝑐𝑐𝑐 +𝜀𝜀𝑖𝑖𝑖𝑖 (4)In equation (2), 𝛼𝛼1 represents the total effect of digital economy development level onrural revitalization. In equation (3), 𝛽𝛽1 indicates the effects of digital economy on theintermediate variable. In equation (4), 𝛾𝛾1 represents the direct effects of digital economy

on rural revitalization, and 𝛽𝛽1 𝛾𝛾2 indicates the indirect effects of digital economy on rural
revitalization. Mediating variables are industrial structure upgrading measured byindustrial structure upgrading index by referring to the studies of Du et al. (2021) and Xu
et al. (2022) and technological innovation measured by patent applications approved forinventions per capita.
Table 10 presents the result of mediation effect model to reveal the process and theintermediate mechanism of the influence of digital economy on rural revitalization.
Columns (1) and (2) show that digital economy measured by e-commerce has asignificantly positive effect on industrial structure upgrading and digital economy has
both direct and indirect effects on rural revitalization. Column (3) and (4) show that while
digital economy measured by e-commerce has a significantly positive effect ontechnological innovation, technological innovation does not have significantly positiveeffect on rural revitalization. Therefore, it is implied that industrial structure upgrading isimportant mechanisms of the digital economy driving rural revitalization.

36

Table 10 Mechanism analysis
(1)

(2)

(3)

Industrial stracture upgrading (isu)(4)

technological innovation
patent applications approved

VARIABLES

taobao100

isu

rural revitalization score

for inventions per capita

rural revitalization score0.181**

0.004***

0.050***

0.004***(0.080)(0.001)(0.016)(0.001)0.002*

isu(0.001)
lnpc_patent_innovation

0.005(0.003)Controls

Yes

Yes

Yes

Yes

County-fixed effect

Yes

Yes

Yes

Yes

city-by-year effect

Yes

Yes

Yes

Yes

Observations

622

622

614

614

Number of county_id

52

52

52

52

Within R-squared

0.738

0.414

0.668

0.431

Between R-squared

0.050

0.394

0.114

0.310

Overall R-squared

0.279

0.399

0.405

0.336

98.18***

99.02***

99.32***

100.44***First-stage F-statistic

10.38

9.42

10.52

9.72

Cragg-Donald Wald F statistic

326.29319.49313.14284.59

14.04

13.75

13.89

14.10

Chi-sq(1) =42.44***

Chi-sq(1) =39.97***

Chi-sq(1) =32.28***First stage
City-level digital economy
variable

Kleibergen-Paap Wald rk F
statisticChi-sq(1)Anderson-Rubin Wald test

= 7.51***37

Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

7.

Conclusions

This paper uses county-level panel data from 2010 to 2021 to measure the comprehensivescore of rural revitalization in Zhejiang by the entropy weighting method and empiricallyanalyzes the effect of digital economy on rural revitalization. This paper finds that mostof the digital economy variables such as e-commerce and digital financial inclusion havea significant positive effect on rural revitalization. This effect does not have significant
regional heterogeneity and robust after addressing the endogeneity problems. This paper
also examines the potential mechanisms by mediation effect model. The results show that
digital economy promoted industrial structure upgrading and technological innovation.
Moreover, the results show that industrial structure upgrading mediates the effects ofdigital economy on rural revitalization, and the mediating effect is partial. Therefore, it isrecommended that the local government accelerate digital industrialization and industrial
digitalization process to realize rural revitalization.

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