Does rural financial development facilitate food production? Evidence from major wheat–producing provinces of China

Figures & data

Figure 1. Distribution of rural credit in selected wheat producing provinces of China.

Figure 2. Province-wise wheat production in China.

Table 1. Summary of empirical studies on the impact of agricultural credit on agricultural production

Country	Time	Model	Crop/Agri. pro	Agri- credit	References
China	1978–2018	ARDL	Cereal production	+/*	He et al. (2022)
China	1990–2017	ARDL	Grain production	+/*	Chandio et al. (2022)
Pakistan	1983–2016	ARDL—VECM	Agri—production	+/*	Chandio et al. (2020)
Vietnam	2004–2016	ARDL	Agricultural GDP	+/*	Anh et al. (2020)
Turkey	1998–2016	OLS	Agri—production	+/*	Bahşi and Çetin (2020)
Pakistan	2017	CDPF	Wheat	+/*	Chandio et al. (2018)
Nepal	1991–2019	VECM	Rice	+/*	Ali Chandio et al. (2022)
China	2000–2020	DOLS-FMOLS	Soybean yield	-/*	Zhang et al. (2022)
US	1991–2010	DID	Agricultural GDP	+/*	Hartarska et al. (2015)
West Africa	1995–2014	CSD	Agricultural GDP	+/*	Kassouri and Kacou (2022)
India	1996–2012	OLS	Agricultural GDP	NS	Narayanan (2016)
Brazil	2022	VECM	Agricultural GDP	+/*	Nascimento et al. (2022)
Nigeria	1981–2016	VECM	Rice	+/*	Omoregie et al. (2018)
Bangladesh	2021	Stochastic frontier	Technical efficiency	+/*	Rabbany et al. (2022)
Cross-country	1991–2014	OLS	Agri—production	+/*	Seven and Tumen (2020)
Pakistan	1971–2011	CDPF	Agricultural GDP	+/*	Shahbaz et al. (2013)
Iran	1970–2011	JCI	Economic growth	+/*	Yazdi and Khanalizadeh (2014)
South Asia	1972–2015	JCI	Agri—production	+/*	Zakaria et al. (2019)

Note: ARDL: Auto Regressive Distributed Lag, VECM: Vertical Error Correction Model, ADF: Augmented Dickey Fuller, P—P: Phillips-Perron, Dynamic, OLS: Ordinary Least Square, JCI: Johansen Co—integration, DID: Difference in Difference, CSD: Cross-sectional dependence, +/*: Positive and significant, NS: Not Significant, CDPF: Cobb—Douglas production function.

Figure 3. Estimation steps.

Figure 4. Stack cross sections data trend.

Table 2. Summary of descriptive statistics and correlation estimates

	lnWP	lnCRED	lnFERC	lnPESC	lnWPA	lnTRA	lnRLF
Mean	6.644	6.941	5.554	1.834	7.529	11.195	7.870
Maximum	8.230	11.713	6.573	9.313	8.655	13.311	8.593
Minimum	5.018	2.679	4.158	−0.150	6.283	8.455	6.834
Std. Dev.	0.834	2.131	0.512	0.936	0.640	1.092	0.463
lnWP	1.000
lnCRED	0.230*	1.000
lnFERC	0.783*	0.418*	1.000
lnPESC	0.496*	0.175*	0.646*	1.000
lnWPA	0.974*	0.057	0.734*	0.481*	1.000
lnTRA	0.428*	0.749*	0.555*	0.327*	0.279*	1.000
lnRLF	0.735*	0.338*	0.840*	0.587*	0.717*	0.310*	1.000

Note: *p < 0.01.

Table 3. CSD tests results

Tests	lnWP	lnCRED	lnFERC	lnPESC	lnWPA	lnTRA	lnRLF
BPLM	412.534*	985.138*	646.532*	443.010*	415.945*	852.079*	583.058*
PSLM	44.3750*	111.857*	71.9519*	47.966*	44.777*	96.175*	64.471*
BCSLM	44.214*	111.696*	71.7912*	47.805*	44.616*	96.0151*	64.310*
PCD	5.831*	31.379*	24.473*	12.742*	5.245*	28.992*	13.695*

Note: *p < 0.01.

Table 4. CADF test results

Tests	lnWP	lnCRED	lnFERC	lnPESC	lnWPA	lnTRA	lnRLF
Level	−1.910	−2.088	−2.050	−1.894	−1.314	−2.382**	−1.973
P-value	(0.321)	(0.155)	(0.185)	(0.339)	(0.916)	(0.027)	(0.149)
First difference	−3.638*	−2.902*	−2.882*	−2.991*	−2.503*	−2.695*	−3.455*
P-value	(0.000)	(0.000)	(0.000)	(0.000)	(0.011)	(0.002)	(0.000)

Note: *p < 0.01 and **p < 0.05.

Table 5. Panel co-integration tests outcomes

Panel (A) Pedroni co-integration test
Statistics	Panel v-Stat	Panel rho-Stat	Panel PP-Stat	Panel ADF-Stat
Value	−0.706324	−0.390726	−11.64023*	−2.601809*
P-value	0.7600	0.3480	0.0000	0.0046
		Group rho-Stat	Group PP-Stat	Group ADF-Stat
Value		1.255665	−9.940556*	−1.718817**
P-value		0.8954	0.0000	0.0428
Panel (B) Kao co-integration test
	ADF
Value	−4.969448*
P-value	0.0000

Note: *p < 0.01 and **p < 0.05.

Table 6. Westerlund ECM panel cointegration test results

Statistic	Gt	Ga	Pt	Pa
Value	−3.679	−8.287	−10.696	−8.853
Z-value	−3.685	2.003	−3.619	0.395
P-value	0.000*	0.977	0.000*	0.654
Robust P-value	0.005*	0.223	0.003*	0.123

Note: *p < 0.01.

Table 7. FGLS regression results

Variables	Coef.	St.Err.	t-value	p-value	[95% Conf	Interval]
lnCRED	0.035*	0.006	5.93	0.000	0.023	0.046
lnFERC	0.032	0.042	0.76	0.445	−0.050	0.114
lnPESC	−0.002	0.005	−0.37	0.711	−0.012	0.008
lnWPA	1.135*	0.029	39.62	0.000	1.079	1.191
lnTRA	0.037*	0.007	5.30	0.000	0.023	0.050
lnRLF	0.083**	0.041	2.01	0.044	0.002	0.163
Constant	−3.363*	0.164	−20.46	0.000	−3.686	−3.041
Wald chi2(6)	10934.76*
Prob > chi2	0.000

Note: *p < 0.01 and **p < 0.05.

Table 8. Robustness check results

	One-step GMM		DOLS
Variables	Coef.	St.Err.	Coef.	St.Err.
lnWP L1.	0.238*	0.054	–––	–––
lnCRED	0.030*	0.006	0.024**	0.014
lnFERC	0.097	0.074	0.073	0.123
lnPESC	−0.041	0.052	−0.072	0.104
lnWPA	0.843*	0.066	1.1372*	0.060
lnTRA	0.014	0.011	0.046**	0.023
lnRLF	0.249*	0.097	0.402**	0.210
Constant	−4.074*	0.743	–––	–––
Wald chi2(7)	1802.26*		R-squared	0.995
Prob > chi2	0.000		Adjusted R^2	0.991

Note: *p < 0.01 and **p < 0.05.

Table 9. Dumitrescu and Hurlin panel causality tests results

Null Hypothesis	W-Stat.	Zbar-Stat.	Prob.
lnCRED does not homogeneously cause lnWP	3.12481	3.72024	0.0002
lnWP does not homogeneously cause lnCRED	1.20283	0.21153	0.8325
lnFERC does not homogeneously cause lnWP	2.44542	2.47997	0.0131
lnWP does not homogeneously cause lnFERC	2.29204	2.19997	0.0278
lnPESC does not homogeneously cause lnWP	2.57138	2.70993	0.0067
lnWP does not homogeneously cause lnPESC	4.34042	5.93942	3.E–09
lnWPA does not homogeneously cause lnWP	3.56395	4.52193	6.E–06
lnWP does not homogeneously cause lnWPA	2.96467	3.42790	0.0006
lnTRA does not homogeneously cause lnWP	4.04839	5.40630	6.E–08
lnWP does not homogeneously cause lnTRA	2.25568	2.13359	0.0329
lnRLF does not homogeneously cause lnWP	2.24886	2.12113	0.0339
lnWP does not homogeneously cause lnRLF	1.42326	0.61394	0.5393

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