Effect of digital economy on large-scale pig farming: An empirical study from China

Figures & data

Figure 1. Research framework of the digital economy on large-scale pig farming.

Table 1. The digital economic index system

Target level	Standard level	Index level	Indicator attribution	Number of observations
Digital Economy	Digital Economy infrastructure	Number of Internet domain names	Positive	248
		Internet broadband access port	Positive	248
		Mobile phone penetration rate	Positive	248
		Length of fiber optic cable line	Positive	248
		Mobile telephone switching system capacity	Positive	248
		Number of computer users	Positive	248
		Internet broadband access users	Positive	248
		The total amount of telecommunication services	Positive	248
		The output value of information services	Positive	248
		Number of people in the information service industry	Positive	248
		Software business revenue	Positive	248
	Industry Digitization	Number of corporate-owned websites	Positive	248
		Number of couriers	Positive	248
		Digital financial inclusion index.	Positive	248
		E-commerce transaction volume	Positive	248
	Digital governance	Years of education per capita	Positive	248
		Number of patents granted	Positive	248
		Technology market turnover	Positive	248
		Number of R&D projects in industrial enterprises above designated size	Positive	248
		R&D expenditures of industrial enterprises above designated size.	Positive	248
		The full-time equivalent of R&D staff in industrial enterprises above the designated size	Positive	248

Table 2. Descriptive statistics of variables

Variable character	Variable name	Symbol	Interpretation	Unit	Mean	Std.Dev.	Min	Max
Explained variable	large-scale pig farming	LSPF	The ratio of the number of farms raising more than 500 pigs to all pig breeding farms	-	0.02	0.02	0	0.14
Explanatory variable	Digital economy	De	The level of digital economy	-	0.11	0.11	0.01	0.71
Mediating variables	Labor costs	Lnalcpp	Ln (average labor cost per pig)	Yuan	4.91	0.46	3.84	5.93
	Merchandise sales	Lntrssc	Ln (total retail sales of social commodities)	10^8 Yuan	8.69	1.05	5.47	10.6
	Technology innovation	Lnperpatent	Ln (number of patents granted per capita)	Item/person	0.01	0.03	0	0.2
Control variables	Feed sources	Lnfeed	Ln (annual production of corn and soybeans)	10^4 tons	5.46	2.04	0	8.44
	Total road mileage	Lnroad	Ln (total road mileage)	10^4 Km	2.43	0.84	0.19	3.5
	The proportion of people with higher education.	Edurate	Percentage of people with university degree or above	-	0.13	0.07	0.02	0.49
	Urbanization rate	Urb	Urbanization rate	%	56.1	13.3	22.7	89.6
	Number of banking institutions	Lnubi	Ln (number of banking institutions)	Unit	8.62	0. 771	6.45	9.76
	Industrial structure	Is	Agricultural GDP/total GDP	-	0.1	0.05	0	0.26
	Livestock economic development level	Lntoli	Ln (total output of livestock industry)	10^8 Yuan	6.4	1.04	3.88	7.87
	Financial payments for agriculture	Lnftp	Ln (agriculture, forestry, animal husbandry and fishery financial transfer payments)	10^8 Yuan	6.05	0.57	4.52	7.18
	Urban and rural residents’ consumption level	Lnclurr	Ln (consumption level of urban and rural residents)	Yuan	9.56	0.38	8.53	10.7

Table 3. Fixed effect regression results

Variables	(1)	(2)	(3)
	LSPF	LSPF	LSPF
De	0.619***	0.053***	0.033*
	(3.79)	(3.16)	(1.85)
Lnfeed		0.003**	0.004***
		(2.12)	(3.06)
Lnroad		−0.015*	−0.023***
		(−1.78)	(−2.71)
Urb		−0.003***	−0.003***
		(−5.05)	(−4.36)
Is		−0.075	−0.130*
		(−1.09)	(−1.86)
Edurate		0.100***	0.080**
		(2.89)	(2.26)
Lntoli		0.019***	0.022***
		(3.28)	(3.35)
Lnubi		−0.007	−0.009
		(−0.73)	(−0.90)
Lnftp		−0.016***	−0.018***
		(−2.71)	(−3.08)
Lnclurr		−0.030	−0.045**
		(−1.54)	(−2.27)
Lnperpatent			0.147*
			(1.67)
Lntrssc			0.008*
			(1.80)
Lnalcpp			−0.025*
			(−1.75)
Constant	0.050***	0.487***	0.746***
	(2.74)	(2.65)	(3.66)
Observations	248	248	248
R-squared	0.265	0.447	0.482
Year-fixed	Yes	Yes	Yes
Province-fixed	Yes	Yes	Yes

Note: t statistics in parentheses; * p < 0.10, ** p < 0.05, *** p < 0.01

Table 4. Robustness test

Variables	(1)	(2)	(3)	(4)	(5)
	LSPF	LSPF (>1,000)	LSPF (>3,000)	LSPF	LSPF
De	0.056***	0.028***	0.010***
	(3.38)	(3.33)	(2.64)
Lnisiov				0.009***	0.010***
				(3.08)	(3.29)
Constant	2.314**	0.359	0.061	0.642***	2.452**
	(2.39)	(0.74)	(0.27)	(3.46)	(2.51)
Observations	248	248	248	248	248
R-squared	0.457	0.513	0.432	0.445	0.455
Control variables	Yes	Yes	Yes	Yes	Yes
Province*Year	Yes	Yes	Yes	No	Yes
Time-fixed	Yes	Yes	Yes	Yes	Yes
Province-fixed	Yes	Yes	Yes	Yes	Yes

Note: t statistic in the parentheses; * p < 0.10, ** p < 0.05.

Table 5. 2SLS Estimate

Variables	(1)2SLS	(2)2SLS
	De	LSPF
Lnoccl	0.057***
	(0.016)
De		0.180**
		(0.089)
Observations	248	248
R-squared	0.641	0.257
Control variables	Yes	Yes
Kleibergen-Paap rk LM statistic	13.408 12.665
Kleibergen-Paap rk Wald F statistic

Note: Robust standard error in the parentheses; ***p<0.01, **p<0.05.

Table 6. Spatial correlation test results of the digital economy on LSPF

Year	Variables	I	z	Variables	I	z
2011	De	−0.083***	−3.059	LSPF	−0.095***	−3.835
2012	De	−0.083***	−3.082	LSPF	−0.092***	−3.712
2013	De	−0.080***	−2.879	LSPF	−0.097***	−3.930
2014	De	−0.081***	−2.907	LSPF	−0.095***	−3.761
2015	De	−0.084***	−3.068	LSPF	−0.089***	−3.465
2016	De	−0.085***	−3.162	LSPF	−0.086***	−3.398
2017	De	−0.087***	−3.277	LSPF	−0.096***	−3.864
2018	De	−0.086***	−3.286	LSPF	−0.073***	−2.547

Note: ***p<0.01.

Table 7. Selection of spatial panel model

Test Method	Statistic Value	P-Value
LR (SDM vs SAR)	84.66	0.000
LR (SDM vs SEM)	82.84	0.000
Wald (SDM vs SAR)	51.44	0.000
Wald (SDM vs SEM)	63.80	0.000
Hausman	chi2(21) = 96.27	0.000

Table 8. Spatial regression results

Variables	(1) SDM*W1	(2) SDM*W2	(3) SLM*W1	(4) SLM*W2
	LSPF	LSPF	LSPF	LSPF
Direct effect
De	0.026	0.073***	0.051***	0.055***
	(1.49)	(4.39)	(3.34)	(3.53)
Indirect effect
De	−0.282**	−0.032*	−0.028***	−0.004**
	(−2.20)	(−1.66)	(−2.90)	(−2.34)
Total effect
De	0.256*	0.042**	0.023**	0.051***
	(1.87)	(2.05)	(2.57)	(3.51)
Observations	248	248	248	248
R-squared	0.464	0.414	0.457	0.456
Control variables	Yes	Yes	Yes	Yes
Year-fixed	Yes	Yes	Yes	Yes
Province-fixed	Yes	Yes	Yes	Yes
Log-likelihood	905.410	905.972	899.424	903.230

Note: t statistics in parentheses, * p < 0.10, ** p < 0.05, *** p < 0.01.

Table 9. Mediation effect test

Variables	(1)	(2)	(3)	(4)	(5)	(6)
	Lnperpatent	Lntrssc	Lnalcpp	LSPF	LSPF	LSPF
De	0.064***	1.425***	−0.609*	0.038*	0.036**	0.057***
	(0.015)	(0.287)	(0.316)	(2.14)	(2.01)	(3.40)
Lnperpatent				0.214**
				(2.59)
Lntrssc					0.011***
					(2.67)
Lnalcpp						−0.029*
						(−1.95)
Constant	−1.219***	−22.603***	1.569	0.534***	0.578***	0.671***
	(0.281)	(5.462)	(6.014)	(2.93)	(3.13)	(3.26)
Observations	248	248	248	248	248	248
R-squared	0.722	0.830	0.586	0.464	0.466	0.457
Control variables	Yes	Yes	Yes	Yes	Yes	Yes
Year-fixed	Yes	Yes	Yes	Yes	Yes	Yes
Province-fixed	Yes	Yes	Yes	Yes	Yes	Yes

Note: t statistics in parentheses, * p < 0.10, ** p < 0.05, *** p < 0.01.

Table 10. Heterogeneity analysis

Variables	(1) Key development area	(2) Potential growth areas	(3) Constrained development area	(4) Moderate development area
	LSPF	LSPF	LSPF	LSPF
De	0.083**	0.116**	−0.087**	0.080
	(0.040)	(0.054)	(0.033)	(0.075)
Constant	−1.665	0.112	3.308***	−0.447*
	(1.482)	(0.374)	(0.769)	(0.250)
Observations	56	48	88	56
R-squared	0.552	0.7609	0.8336	0.8194
Control variables	Yes	Yes	Yes	Yes
Year-fixed	Yes	Yes	Yes	Yes
Province-fixed	Yes	Yes	Yes	Yes

Note: t statistics in parentheses, * p < 0.10, ** p < 0.05, *** p < 0.01.

Table

Year	City	Score	Year	City	Score	Year	City	Score
2011	Beijing	0.1412	2011	Henan	0.0584	2011	Jiangxi	0.0303
2012	Beijing	0.1720	2012	Henan	0.0711	2012	Jiangxi	0.0366
2013	Beijing	0.2071	2013	Henan	0.0877	2013	Jiangxi	0.0439
2014	Beijing	0.2414	2014	Henan	0.1048	2014	Jiangxi	0.0504
2015	Beijing	0.2930	2015	Henan	0.1234	2015	Jiangxi	0.0624
2016	Beijing	0.3435	2016	Henan	0.1356	2016	Jiangxi	0.0703
2017	Beijing	0.3564	2017	Henan	0.1555	2017	Jiangxi	0.0814
2018	Beijing	0.3889	2018	Henan	0.2001	2018	Jiangxi	0.1197
2011	Shanghai	0.1143	2011	Shaanxi	0.0366	2011	Hubei	0.0556
2012	Shanghai	0.1354	2012	Shaanxi	0.0481	2012	Hubei	0.0696
2013	Shanghai	0.1526	2013	Shaanxi	0.0609	2013	Hubei	0.0849
2014	Shanghai	0.1690	2014	Shaanxi	0.0708	2014	Hubei	0.0990
2015	Shanghai	0.1990	2015	Shaanxi	0.0807	2015	Hubei	0.1259
2016	Shanghai	0.2212	2016	Shaanxi	0.0902	2016	Hubei	0.1354
2017	Shanghai	0.2369	2017	Shaanxi	0.1019	2017	Hubei	0.1465
2018	Shanghai	0.2599	2018	Shaanxi	0.1212	2018	Hubei	0.1774
2011	Tianjin	0.0450	2011	Gansu	0.0131	2011	Sichuan	0.0641
2012	Tianjin	0.0548	2012	Gansu	0.0180	2012	Sichuan	0.0823
2013	Tianjin	0.0629	2013	Gansu	0.0225	2013	Sichuan	0.0971
2014	Tianjin	0.0718	2014	Gansu	0.0262	2014	Sichuan	0.1142
2015	Tianjin	0.0815	2015	Gansu	0.0310	2015	Sichuan	0.1347
2016	Tianjin	0.0886	2016	Gansu	0.0331	2016	Sichuan	0.1546
2017	Tianjin	0.0880	2017	Gansu	0.0407	2017	Sichuan	0.1729
2018	Tianjin	0.1006	2018	Gansu	0.0502	2018	Sichuan	0.2118
2011	Chongqing	0.0371	2011	Ningxia	0.0091	2011	Guizhou	0.0168
2012	Chongqing	0.0463	2012	Ningxia	0.0124	2012	Guizhou	0.0230
2013	Chongqing	0.0566	2013	Ningxia	0.0147	2013	Guizhou	0.0291
2014	Chongqing	0.0667	2014	Ningxia	0.0168	2014	Guizhou	0.0348
2015	Chongqing	0.0782	2015	Ningxia	0.0185	2015	Guizhou	0.0386
2016	Chongqing	0.0891	2016	Ningxia	0.0209	2016	Guizhou	0.0432
2017	Chongqing	0.0980	2017	Ningxia	0.0249	2017	Guizhou	0.0541
2018	Chongqing	0.1151	2018	Ningxia	0.0298	2018	Guizhou	0.0697
2011	Heilongjiang	0.0410	2011	Qinghai	0.0068	2011	Fujian	0.0694
2012	Heilongjiang	0.0467	2012	Qinghai	0.0097	2012	Fujian	0.0867
2013	Heilongjiang	0.0627	2013	Qinghai	0.0120	2013	Fujian	0.0953
2014	Heilongjiang	0.0662	2014	Qinghai	0.0137	2014	Fujian	0.1093
2015	Heilongjiang	0.0671	2015	Qinghai	0.0155	2015	Fujian	0.1376
2016	Heilongjiang	0.0630	2016	Qinghai	0.0167	2016	Fujian	0.1950
2017	Heilongjiang	0.0665	2017	Qinghai	0.0192	2017	Fujian	0.2647
2018	Heilongjiang	0.0801	2018	Qinghai	0.0228	2018	Fujian	0.2727
2011	Jilin	0.0229	2011	Xinjiang	0.0414	2011	Guangdong	0.2405
2012	Jilin	0.0289	2012	Xinjiang	0.0458	2012	Guangdong	0.3054
2013	Jilin	0.0354	2013	Xinjiang	0.0499	2013	Guangdong	0.3527
2014	Jilin	0.0373	2014	Xinjiang	0.0539	2014	Guangdong	0.3865
2015	Jilin	0.0406	2015	Xinjiang	0.0574	2015	Guangdong	0.4483
2016	Jilin	0.0462	2016	Xinjiang	0.0607	2016	Guangdong	0.5132
2017	Jilin	0.0538	2017	Xinjiang	0.0756	2017	Guangdong	0.5797
2018	Jilin	0.0609	2018	Xinjiang	0.0858	2018	Guangdong	0.7066
2011	Liaoning	0.0659	2011	Anhui	0.0512	2011	Guangxi	0.0234
2012	Liaoning	0.0788	2012	Anhui	0.0662	2012	Guangxi	0.0291
2013	Liaoning	0.0884	2013	Anhui	0.0797	2013	Guangxi	0.0342
2014	Liaoning	0.0955	2014	Anhui	0.0899	2014	Guangxi	0.0415
2015	Liaoning	0.1025	2015	Anhui	0.1053	2015	Guangxi	0.0461
2016	Liaoning	0.0957	2016	Anhui	0.1208	2016	Guangxi	0.0522
2017	Liaoning	0.1046	2017	Anhui	0.1358	2017	Guangxi	0.0654
2018	Liaoning	0.1153	2018	Anhui	0.1661	2018	Guangxi	0.0796
2011	Inner Mongolia	0.0253	2011	Jiangsu	0.2419	2011	Hainan	0.0108
2012	Inner Mongolia	0.0328	2012	Jiangsu	0.2900	2012	Hainan	0.0150
2013	Inner Mongolia	0.0373	2013	Jiangsu	0.3168	2013	Hainan	0.0202
2014	Inner Mongolia	0.0397	2014	Jiangsu	0.3289	2014	Hainan	0.0233
2015	Inner Mongolia	0.0427	2015	Jiangsu	0.3711	2015	Hainan	0.0264
2016	Inner Mongolia	0.0454	2016	Jiangsu	0.4112	2016	Hainan	0.0262
2017	Inner Mongolia	0.0489	2017	Jiangsu	0.4448	2017	Hainan	0.0333
2018	Inner Mongolia	0.0584	2018	Jiangsu	0.5066	2018	Hainan	0.0390
2011	Hebei	0.0492	2011	Zhejiang	0.1558	2011	Yunnan	0.0210
2012	Hebei	0.0610	2012	Zhejiang	0.2290	2012	Yunnan	0.0275
2013	Hebei	0.0694	2013	Zhejiang	0.2194	2013	Yunnan	0.0339
2014	Hebei	0.0791	2014	Zhejiang	0.2422	2014	Yunnan	0.0382
2015	Hebei	0.0919	2015	Zhejiang	0.3034	2015	Yunnan	0.0435
2016	Hebei	0.1041	2016	Zhejiang	0.3593	2016	Yunnan	0.0491
2017	Hebei	0.1166	2017	Zhejiang	0.3833	2017	Yunnan	0.0577
2018	Hebei	0.1446	2018	Zhejiang	0.4503	2018	Yunnan	0.0754
2011	Shanxi	0.0290	2011	Hunan	0.0585	2011	Tibet	0.0055
2012	Shanxi	0.0350	2012	Hunan	0.0711	2012	Tibet	0.0088
2013	Shanxi	0.0409	2013	Hunan	0.0834	2013	Tibet	0.0114
2014	Shanxi	0.0438	2014	Hunan	0.0946	2014	Tibet	0.0136
2015	Shanxi	0.0467	2015	Hunan	0.1070	2015	Tibet	0.0151
2016	Shanxi	0.0493	2016	Hunan	0.1282	2016	Tibet	0.0182
2017	Shanxi	0.0574	2017	Hunan	0.1434	2017	Tibet	0.0207
2018	Shanxi	0.0790	2018	Hunan	0.1872	2018	Tibet	0.0245
2011	Shandong	0.1303
2012	Shandong	0.1559
2013	Shandong	0.2339
2014	Shandong	0.2336
2015	Shandong	0.2417
2016	Shandong	0.2607
2017	Shandong	0.2740
2018	Shandong	0.3160

Data availability statement

The datasets analyzed during the current study are available from the corresponding author on reasonable request or can be collected from https://data.cnki.net/.