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How Digital Financial Inclusion Based on Spatial Durbin Modeling Affects the Rural-Urban Gap

Sanbao Zhang

Sanbao Zhang

School of Finance, Tongling UniversityTongling, China
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Zhang, Sanbao
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Chaojie Zhang

Chaojie Zhang

Department of Big Data and Artificial Intelligence, Wuhu UniversityWuhu, China
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Zhang, Chaojie
  
Mar 19, 2025
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Published Online: Mar 19, 2025
Received: Oct 15, 2024
Accepted: Feb 03, 2025
DOI: https://doi.org/10.2478/amns-2025-0509
Keywords
© 2025 Sanbao Zhang et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

In 2012, the consumption gap of urban and rural residents in city
In 2012, the consumption gap of urban and rural residents in city

Figure 2.

In 2019, the consumption gap of urban and rural residents in city
In 2019, the consumption gap of urban and rural residents in city

Figure 3.

In 2012, some local digital market Numbers, puhui financial development
In 2012, some local digital market Numbers, puhui financial development

Figure 4.

In 2019, some local digital market Numbers, puhui financial development
In 2019, some local digital market Numbers, puhui financial development

LM test results

Test type Statistic P value
LM-lag 15.411 0.000
RobustLM-lag 5.234 0.026
LM-error 25.783 0.000
RobustLM-error 15.615 0.000

SDM model regression results

Variable Regression coefficient P value
ρ 0.6444*** 0.000
difi -0.0186*** 0.000
urban -0.1009*** 0.002
is 0.0892** 0.014
open -0.0467*** 0.000
fe 0.0365** 0.013
edu 0.0046** 0.032
rgdp 0.0018** 0.011
afe -0.0023 0.956
work -0.0498** 0.012
W*difi 0.0175** 0.000
W*urban -0.1115** 0.047
W*is -0.0369 0.165
W*open 0.0368*** 0.005
W*fe 0.0295 0.326
W*edu -0.0018 0.679
W*rgdp 0.0013 0.467
W*afe 0.1118* 0.084
W*work 0.0385 0.204
R-sq 0.7397
Log-L 1053.7568

The classification of the gap between urban and rural in 160 cities

Year Urban and rural income gap difi
Moran’sI z Moran’sI z
2012 0.458*** 8.351 0.539*** 9.689
2013 0.447*** 8.052 0.535*** 9.678
2014 0.442*** 8.033 0.556*** 10.019
2015 0.436*** 7.886 0.489*** 8.795
2016 0.406*** 7.306 0.536*** 9.688
2017 0.365*** 6.584 0.515*** 9.325
2018 0.361*** 6.579 0.529*** 9.536
2019 0.398*** 7.116 0.584*** 10.548

The effect of the dubin model (SD,) decomposition

Variable Direct effect P value Indirect effect P value Total effect P value
difi -0.0175*** 0.000 0.0135** 0.025 -0.0037** 0.028
urban -0.1396*** 0.000 -0.4478*** 0.006 -0.5876*** 0.000
is 0.0915** 0.021 -0.0176 0.887 0.0741 0.612
open -0.04656*** 0.000 0.0178 0.598 -0.0278 0.456
fe 0.0478*** 0.004 0.1345* 0.075 0.1815** 0.027
edu 0.0049** 0.035 0.0033 0.748 0.0078 0.479
rgdp 0.0023*** 0.012 0.0056 0.145 0.0075* 0.085
afe 0.0206 0.598 0.2836* 0.086 0.3038* 0.098
work -0.0476** 0.028 0.0137 0.867 -0.0339 0.716

Wald, LR test results

Test type Statistic P value
Wald-spacial-lag 6.85 0.0095
Wald-spacial-error 9.69 0.0023
LR-spacial-lag 64.55 0.0000
LR-spacial-error 91.26 0.0000
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