Does FinTech credit scale stimulate financial institutions to increase the proportion of agricultural loans?

Figures & data

Table 1. Descriptive statistics of main variables

Variables	Mean	Standard Deviations	Minimum Value	Maximum Value
$\mathrm{r}\mathrm{a}\mathrm{r}\mathrm{g}\mathrm{l}\mathrm{o}\mathrm{a}{\mathrm{n}}_{\mathrm{i}\mathrm{t}}$	28.9727	11.2492	2.6312	46.3149
$m\mathrm{F}\mathrm{i}\mathrm{n}\mathrm{T}\mathrm{e}\mathrm{c}\mathrm{h}{}_{it}$	0.2098	0.8821	0.0000	5.8019
urbrateit	0.5437	0.1380	0.2273	0.8961
ggdpit	10.0403	2.8002	3.1000	16.9000
agdpit	1.7048	1.2288	0.0745	4.8327
people	4.3719	2.7536	0.3033	10.8490
aloanbit	3.1988	0.8221	2.2700	4.5200
runempit	3.3799	0.6536	1.3000	4.4000
rssalesit	2.5361	2.4234	0.0074	13.0669
readjustt	0.2222	0.416487	0	1
encrgt	0.2222	0.416487	0	1
fgdpit	1.2497	1.2605	0.0317	6.1271
dlrate	2.4497	6.1186	0.0037	55.9140
mktproit	6.1338	2.0834	0.06	9.9500
rmbphn	0.0858	0.0257	0.0363	0.1781
iuserseit	0.1925	0.1436	0.0090	0.7768
eduleve	52.2658	13.9844	22.6302	99.0054
dassit	0.1398	0.347387	0	1
gp	1.5392	1.932914	−0.7965	12.8442
mpt	4.4667	5.564086	−1.3000	18.9800
	5	2.586629	1	9

Table 2. FE regression results of model (a)

Variables	(1) rargloanit	(2) rargloanit	(3) rargloanit	(4) rargloanit
$\sqrt{m\mathrm{F}\mathrm{i}\mathrm{n}\mathrm{T}\mathrm{e}\mathrm{c}\mathrm{h}{}_{it}}$	1.629**	1.485**	1.718**	1.650**
	(0.690)	(0.661)	(0.689)	(0.643)
urbrateit	−3.011	−6.447	−4.838	−8.572
	(15.030)	(15.040)	(15.090)	(14.510)
ggdPit-1	−0.0521	−0.0601	−0.159	−0.135
	(0.118)	(0.120)	(0.130)	(0.130)
lagdPit-1	5.063**	6.282**	6.523**	6.946***
	(2.433)	(2.571)	(2.522)	(2.509)
peopleit	−7.756**	−8.255***	−8.212***	−7.887***
	(3.051)	(2.995)	(2.891)	(2.852)
rssalesit	−1.148***	−1.187***	−1.120**	−1.178***
	(0.435)	(0.435)	(0.447)	(0.437)
rssales2it	0.0273	0.0291	0.0254	0.0298
	(0.0285)	(0.0283)	(0.0297)	(0.0284)
treadjustt	−0.269***	−0.251***	−0.324***	M.319***
	(0.0787)	(0.0803)	(0.0870)	(0.0869)
tencrgt	−0.158**	−0.164**	−0.175**	−0.177***
	(0.0679)	(0.0685)	(0.0684)	(0.0672)
fgdpit-1	−0.291	−0.358	−0.403	−0.334
	(0.473)	(0.465)	(0.491)	(0.469)
dlrateit	−0.0135	−0.0141	−0.0112	−0.00956
	(0.0203)	(0.0201)	(0.0211)	(0.0199)
mktproit	6.961**	6.537***	6.700***	7.070***
	(1.787)	(1.728)	(1.711)	(1.612)
mktpro2it	−0.414***	−0.381*”	−0.397*”	−0.418*”
	(0.126)	(0.121)	(0.120)	(0.112)
rmbphnit	−32.16	−73.19	−98.46*	−124.0**
	(55.16)	(55.75)	(56.89)	(55.27)
rmbphn2it	56.73	209.1	287.7	373.2*
	(213.8)	(209.3)	(210.7)	(204.9)
iusersit	69.68***	75.75***	74.02***	80.87***
	(20.42)	(19.61)	(20.16)	(19.33)
iusers2it	−33.48***	−37.66***	−36.60***	−41.93***
	(16.76)	(16.13)	(16.66)	(15.77)
edulevit	−0.0452	−0.0307	−0.021	−0.0177
	(0.0441)	(0.0423)	(0.0407)	(0.0400)
gpit		−0.591	−0.656	−0.551
		(0.551)	(0.513)	(0.468)
mpt			−0.134***	−0.00677
			(0.0545)	(0.0635)
gmpit				−0.0785***
				(0.0272)
Constant	32.25**	38.23***	39.47***	38.27**
	(14.10)	(13.75)	(13.46)	(13.34)
Observations	248	248	248	248
Number of provinces	31	31	31	31
Adjust R^2	0.503	0.507	0.517	0.544

***, **, and * represent the significance levels of 1%, 5%, and 10%, respectively. The double clustering robust standard errors are in parentheses.

Table 3. Test results of the mediation effect of the currency marketization level as the mediation factor

Path A (without intermediary factors)	${\mathrm{r}\mathrm{a}\mathrm{r}\mathrm{g}\mathrm{l}\mathrm{o}\mathrm{a}\mathrm{n}}_{\mathrm{i}\mathrm{t}}$	Path B (Intermediary Subtest)	$\sqrt{m\mathrm{F}\mathrm{i}\mathrm{n}\mathrm{T}\mathrm{e}\mathrm{c}\mathrm{h}{}_{it}}$	Path C (with intermediary factors)	${\mathrm{r}\mathrm{a}\mathrm{r}\mathrm{g}\mathrm{l}\mathrm{o}\mathrm{a}\mathrm{n}}_{\mathrm{i}\mathrm{t}}$
$mktpr{o}_{it}$	6.828***	$mktpr{o}_{it}$	−0.147	$\sqrt{m\mathrm{F}\mathrm{i}\mathrm{n}\mathrm{T}\mathrm{e}\mathrm{c}\mathrm{h}{}_{it}}$	1.650**
$mktpro{2}_{it}$	(1.650)		(0.122)		(0.643)
	−0.390***	$mktpro{2}_{it}$	0.0173*		7.070***
	(0.113)		(0.0104)		(1.612)
				$mktpro{2}_{it}$	−0.418***
Observations	248	Observations	248	Observations	248
Number of provinces	31	Number of provinces	31	Number of provinces	31
Adjust R^2	0.533	Adjust R^2	0.784	Adjust R^2	0.544

***, **, and * represent the significance levels of 1%, 5%, and 10%, respectively. The double clustering robust standard errors are in parentheses.

Table 4. Test results of the intermediary effect of real estate sales by the intermediary factor

Path A	rargloanit	Path B	$\sqrt{m\mathrm{F}\mathrm{i}\mathrm{n}\mathrm{T}\mathrm{e}\mathrm{c}\mathrm{h}{}_{it}}$	Path C	rargloanit
(Without intermediary factors)		(Intermediary Subtest)	(Intermediary Subtest)	(With intermediary factors)
rssalesit	−1.113***	rssalesit	0.0704*		1.650**
$\mathrm{r}\mathrm{s}\mathrm{s}\mathrm{a}\mathrm{l}\mathrm{e}\mathrm{s}{2}_{it}$	(0.437)		(0.0412)		(0.643)
	0.0335			rssalesit	−1.178***
	(0.0278)				(1.612)
				$\mathrm{r}\mathrm{s}\mathrm{s}\mathrm{a}\mathrm{l}\mathrm{e}\mathrm{s}{2}_{it}$	0.030
					(0.0284)
Observations	248	Observations	248		248
Number of provinces	31	Number of provinces	31	Number of provinces	31
Adjust R^2	0.533	Adjust R^2	0.784	Adjust R^2	0.544

***, **, and * represent the significance levels of 1%, 5%, and 10%, respectively. The double clustering robust standard errors are in parentheses.