Rural financial development, spatial spillover, and poverty reduction: evidence from China

Figures & data

Figure 1. National trends in China’s official poverty headcount, 1978–2015.

Source: The Central People’s Government (2018).

Table 1. Definitions, codes, and measurement methods of the variables. 

Name of variable	Definition of variable	Calculation
POVERTY	Poverty level	Food consumption / gross family expenditure
FINANCIAL	Rural financial development	Rural credit scale / gross agricultural output value
URBAN	Urbanization	Urban population / total population
FISCAL	Fiscal expenditure for agriculture development	Fiscal expenditure for agriculture development / gross agricultural output value
MECHANIZATION	Mechanization	Total agricultural machinery power in rural areas / number of rural labourers
FASSET	Fixed asset investment	Fixed asset investment in rural areas / gross agricultural output value
AAOUTPUT	Average agricultural output value	Gross agricultural output value / number of rural labourers
RELIEF	Rural social relief	Rural social relief / total fiscal expenditure
DISATER	Agricultural disaster area	Agricultural disaster area / total sowing area
HIGHWAY	Highway mileage	Highway mileage (10,000 km)

Source: Authors' definition.

Table 2. Summary statistics of the variables.

Code	Obs	Mean	Std. Dev	Min	Max
POVERTY	570	0.440	0.086	0.273	0.696
FINANCIAL	570	0.686	0.831	0.027	5.510
URBAN	570	0.414	0.189	0.110	0.901
FISCAL	570	0.089	0.032	0.021	0.236
MECHANIZATION	570	1.031	0.675	0.151	3.817
FASSET	570	0.110	0.050	0.001	0.302
AAOUTPUT	570	0.798	0.641	0.127	1.415
RELIEF	570	0.010	0.020	0.0001	0.212
DISATER	570	0.270	0.164	0.000	0.936
HIGHWAY	570	9.040	6.684	0.396	31.558

Source: Authors' calculation.

Table 3. Spatial autocorrelation test of the poverty level from 1997 to 2015.

Year	Moran’s I	Year	Moran’s I
1997	0.417***	2007	0.602***
1998	0.470***	2008	0.610***
1999	0.597***	2009	0.555***
2000	0.621***	2010	0.681***
2001	0.603***	2011	0.558***
2002	0.591***	2012	0.546***
2003	0.606***	2013	0.564***
2004	0.579***	2014	0.549***
2005	0.591***	2015	0.516***
2006	0.683***	—	—

Note: ***, **, and *denote significance at the 1%, 5%, and 10% level, respectively.

Source: Authors' calculation.

Table 4. Panel unit root test.

Variables	IPS (Level)	IPS (First difference)	HT (Level)	HT (First difference)
POVERTY	−5.282***		0.504***
FINANCIAL	−2.341***		0.574**
URBAN	−0.914	−7.747***	0.755	0.427***
FISCAL	−7.199***		0.110***
MECHANIZATION	2.580	−8.738***	0.794	0.330***
FASSET	−3.188***	−6.433***	0.725***
AAOUTPUT	2.262	−7.804***	0.909	0.413***
RELIEF	16.502	−6.739***	1.366	0.497***
DISATER	−11.129***		−0.137***
HIGHWAY	4.327	−10.749***	0.607	0.080***

Note: ***, **, and * denote significance at the 1%, 5%, and 10% level, respectively.

Source: Authors' calculation.

Table 5. Panel cointegration test.

Pedroni test	Category	(0,0)	(1,0)	(1,1)
	Panel v-Statistic	−4.512***	−1.770**	−1.830**
	Panel rho-Statistic	2.258**	1.024	1.857**
	Panel PP-Statistic	−1.205	−3.523***	−4.734***
	Panel ADF-Statistic	−2.168**	−4.393***	−5.921***
	Group rho-Statistic	4.014***	2.663***	3.894***
	Group PP-Statistic	−1.492*	−3.758***	−3.601***
	Group ADF-Statistic	−2.978***	−4.736***	−5.182***
Kao test	ADF-statistic	—	−5.6781***	—

Note: Both the Pedroni test and the Kao test hypothesize that there is no cointegration relationship. In the Pedroni test, the first four and last three rows report the results within dimensions and between dimensions, respectively. The three columns to the right exhibit the results accounting for neither individual fixed effect nor linear time trend (column (0,0)), for individual fixed effect only (column (0,1)), and for both separately (column (1,1)).

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

Source: Authors' calculation.

Table 6. Regression results of rural financial development on poverty level.

Variables	(1) FE	(2) SDM	(3) Direct	(4) Indirect	(5) Total
FINANCIAL	−0.094**	−0.048**	−0.063***	−0.139**	−0.202**
	(0.039)	(0.020)	(0.022)	(0.068)	(0.079)
URBAN	−0.026	−0.046*	−0.005	0.380***	0.375***
	(0.042)	(0.023)	(0.023)	(0.115)	(0.127)
FISCAL	0.088	−0.179**	−0.070	0.926**	0.855**
	(0.120)	(0.078)	(0.090)	(0.379)	(0.423)
MECHANIZATION	−0.017	−0.011*	−0.016**	−0.048**	−0.064**
	(0.017)	(0.006)	(0.008)	(0.024)	(0.029)
FASSET	−0.378**	−0.417***	−0.488***	−0.560***	−1.048***
	(0.147)	(0.038)	(0.053)	(0.140)	(0.179)
AAOUTPUT	−0.039***	−0.031***	−0.036***	−0.048**	−0.084***
	(0.010)	(0.006)	(0.006)	(0.019)	(0.021)
RELIEF	−0.547***	−0.353***	−0.427***	−0.651**	−1.079***
	(0.099)	(0.084)	(0.077)	(0.293)	(0.303)
DISATER	0.022	0.012	0.007	−0.031	−0.025
	(0.014)	(0.010)	(0.009)	(0.030)	(0.033)
HIGHWAY	−0.005***	−0.003***	−0.004***	−0.007***	−0.011***
	(0.001)	(0.001)	(0.000)	(0.002)	(0.002)
Constant	0.639***
	(0.041)
ρ		5900.964***
		(422.093)
Wald test		19.01***
LR test		27.33***
R-squared	0.478	0.571
Observations	570	570

Note: The standard error is in parentheses.

***, **, and * denote significance at the 1%, 5%, and 10% level, respectively. The spatial lag explanatory variables of the SDM model are reported in Appendix Table A1, column (1).

Source: Authors' calculation.

Table 7. Results of robustness check.

Variables	(1) 2SLS	Using rural financial efficiency as a proxy variable of FINANCIAL
		(2) SDM	(3) Direct	(4) Indirect	(5) Total
FINANCIAL	−0.078**	−0.019***	−0.021***	−0.017**	−0.038***
	(0.031)	(0.003)	(0.004)	(0.007)	(0.009)
URBAN	−0.013	−0.058***	−0.008	0.470***	0.462***
	(0.025)	(0.023)	(0.022)	(0.114)	(0.125)
FISCAL	−0.023	−0.160**	−0.056	0.903**	0.847**
	(0.091)	(0.076)	(0.087)	(0.362)	(0.402)
MECHANIZATION	−0.013*	−0.019***	−0.028***	−0.081***	−0.109***
	(0.007)	(0.006)	(0.008)	(0.025)	(0.031)
FASSET	−0.373***	−0.397***	−0.447***	−0.381***	−0.828***
	(0.051)	(0.038)	(0.052)	(0.130)	(0.168)
AAOUTPUT	−0.039***	−0.029***	−0.032***	−0.021	−0.053**
	(0.006)	(0.005)	(0.006)	(0.021)	(0.023)
RELIEF	−0.554***	−0.326***	−0.396***	−0.624**	−1.019***
	(0.085)	(0.083)	(0.078)	(0.273)	(0.282)
DISATER	0.016	0.009	0.002	−0.050	−0.048
	(0.012)	(0.010)	(0.009)	(0.031)	(0.033)
HIGHWAY	−0.005***	−0.003***	−0.004***	−0.007***	−0.010***
	(0.001)	(0.001)	(0.000)	(0.002)	(0.002)
Constant	0.624***
	(0.026)
Anderson Canon LM	300.811***
Cragg-Donald Wald F	720.434
ρ		5797.702***
		(428.966)
R-squared	0.420	0.077
Observations	540	570

Note: The standard error is in parentheses.

***, **, and *denote significance at the 1%, 5%, and 10% level, respectively. The spatial lag explanatory variables of the SDM model are reported in Appendix Table A1, column (2).

Source: Authors' calculation.

Table 8. Regression results of rural financial development on urban-rural income inequality.

Variables	Urban-rural income inequality			Per capita income of urban residents (10,000 yuan)			Per capita income of rural residents (10,000 yuan)
	(1) Direct	(2) Indirect	(3) Total	(4) Direct	(5) Indirect	(6) Total	(7) Direct	(8) Indirect	(9) Total
FINANCIAL	0.080***	0.156***	0.236***	0.049***	0.011***	0.059***	0.014***	0.002	0.016***
	(0.027)	(0.044)	(0.059)	(0.003)	(0.003)	(0.005)	(0.001)	(0.002)	(0.002)
URBAN	−0.169	−1.919***	−2.088**	0.182***	−0.135**	0.047	0.044***	−0.022	0.022
	(0.164)	(0.739)	(0.811)	(0.018)	(0.055)	(0.054)	(0.006)	(0.026)	(0.027)
FISCAL	−1.184*	−2.882	−4.066*	0.098	−0.949***	−0.851***	0.000	−0.310***	−0.309***
	(0.643)	(2.210)	(2.433)	(0.077)	(0.178)	(0.182)	(0.028)	(0.086)	(0.093)
MECHANIZATION	0.236***	1.046***	1.282***	−0.017***	0.054***	0.037***	−0.012***	−0.008	−0.021***
	(0.057)	(0.176)	(0.209)	(0.006)	(0.011)	(0.014)	(0.002)	(0.005)	(0.007)
FASSET	−0.595	−1.300*	−1.895*	−0.025	−0.131**	−0.156*	0.015	0.039	0.055
	(0.380)	(0.733)	(1.009)	(0.043)	(0.054)	(0.080)	(0.016)	(0.028)	(0.040)
AAOUTPUT	−0.149***	−0.534***	−0.683***	0.082***	0.028**	0.110***	0.042***	0.036***	0.078***
	(0.042)	(0.137)	(0.153)	(0.005)	(0.011)	(0.011)	(0.002)	(0.005)	(0.006)
RELIEF	−3.044***	−6.495***	−9.539***	0.047	0.760***	0.807***	0.048*	0.505***	0.553***
	(0.608)	(1.814)	(1.848)	(0.080)	(0.145)	(0.126)	(0.028)	(0.068)	(0.067)
DISATER	0.207***	0.105	0.311	0.017**	−0.023	−0.006	0.002	0.001	0.003
	(0.067)	(0.202)	(0.211)	(0.008)	(0.016)	(0.016)	(0.003)	(0.008)	(0.008)
HIGHWAY	0.002	0.044***	0.046***	0.002***	0.003***	0.004***	0.001***	−0.000	0.000
	(0.004)	(0.010)	(0.011)	(0.000)	(0.001)	(0.001)	(0.000)	(0.000)	(0.000)
ρ	5102***			1643***			4294***
	526.112			483.322			489.310
R-squared	0.023			0.741			0.710
Observations	570			570			570

Note: The standard error is in parentheses.

***, **, and *denote significance at the 1%, 5%, and 10% levels, respectively. The spatial lag explanatory variables of the SDM model are reported in Appendix Table A1, column (3) to (5). Yuan is Chinese currency unit, 1 Yuan ≈0.15 US dollar in 2020.

Source: Authors' calculation.

Table A1. Estimates of spatial lag explanatory variables of SDM model.

Variables	(1) SDM	(2) SDM	(3) SDM	(4) SDM	(5) SDM
W*FINANCIAL	−707.974	−15.328	1049.200***	72.384	−34.987**
	(496.548)	(44.025)	(341.123)	(46.046)	(16.469)
W*URBAN	2937.943***	3752.501***	−17437.694***	−2245.036***	−441.003*
	(713.589)	(718.927)	(5527.041)	(683.807)	(242.940)
W*FISCAL	7863.626***	7766.501***	−20333.741	−13312.046***	−3215.234***
	(2450.840)	(2419.899)	(18230.862)	(2255.035)	(800.057)
W*MECHANIZATION	−263.453	−474.208***	8656.073***	800.661***	−30.583
	(174.317)	(166.884)	(1297.776)	(167.375)	(60.764)
W*FASSET	−1588.616*	−538.929	−9878.592	−1866.464**	318.987
	(823.814)	(814.888)	(6422.644)	(781.079)	(277.492)
W*AAOUTPUT	−167.675	9.439	−4224.951***	251.442	205.776***
	(142.997)	(144.409)	(1119.691)	(159.163)	(68.833)
W*RELIEF	−2661.014	−2805.910	−45910.248***	10448.813***	5079.205***
	(2088.708)	(2031.450)	(15936.823)	(2018.060)	(765.488)
W*DISATER	−313.635	−447.925*	−336.407	−381.631	−3.438
	(245.230)	(254.798)	(1973.367)	(244.204)	(86.742)
W*HIGHWAY	−34.338**	−33.649***	397.252***	32.847**	−4.129
	(13.687)	(13.035)	(103.323)	(13.226)	(4.392)

Note: The standard error is in parentheses.

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

Source: Authors' calculation.