Village-level multidimensional poverty measurement in China: Where and how

doi:10.1007/s11442-018-1555-0

Abstract

Abstract:

Village is an important implementation unit of national poverty alleviation and development strategies of rural China, and identifying the poverty degree, poverty type and poverty contributing factors of each poverty-stricken village is the precondition and guarantee of taking targeted measures in poverty alleviation strategies of China. To respond it, we construct a village-level multidimensional poverty measuring model, and use indicator contribution degree indices and linear regression method to explore poverty factors, while adopting Least Square Error (LSE) model and spatial econometric analysis model to identify the villages’ poverty types and poverty difference. The case study shows that: (1) Spatially, there is obvious territoriality in the distribution of poverty-stricken villages, and the poverty-stricken villages are concentrated in contiguous poverty-stricken areas. The areas with the highest VPI, in a descending order, are Gansu, Yunnan, Guizhou, Guangxi, Hunan, Qinghai, Sichuan, and Xinjiang. (2) The main factors contributing to the poverty of poverty-stricken villages in rural China include road construction, terrain type, frequency of natural disasters, per capita net income, labor force ratio, and cultural quality of labor force. The main causes of poverty include underdeveloped road construction conditions, frequent natural disasters, low level of income, and labor conditions. (3) Chinese poverty-stricken villages include six main subtypes, and most poverty-stricken villages are affected by multiple poverty-forming factors, reflected by a relatively high proportion of the three-factor dominant type, four-factor coordinative type, and five-factor combinative type. (4) There exist significant poverty differences in terms of geographical location and policy support, and the governments still need to carry out targeted poverty alleviation measures according to local conditions. The research can not only draw a macro overall poverty-reduction outline of impoverished villages in China, but also depict the specific poverty characteristics of each village, helping the government departments of poverty alleviation at all levels to mobilize all kinds of anti-poverty resources.

Key words: poor village, multidimensional poverty measurement, poverty type, poverty factors, spatial econometric analysis

Yanhui WANG, Yefeng CHEN, Yao CHI, Wenji ZHAO, Zhuowei HU, Fuzhou DUAN. Village-level multidimensional poverty measurement in China: Where and how[J].Journal of Geographical Sciences, 2018, 28(10): 1444-1466.

Figures/Tables 11

Figure 1

Table 1

Village-level multidimensional poverty measurement indicators"

Dimension	No.	Indicator	Indicator implication
Geographical environment (X₁)	X₁₁	Distance from the nearest town’s bazaar	The distance from the village to the nearest town’s bazaar (km)
	X₁₂	Terrain type	Terrain type of the village (namely, plain, hilly, plateau, basin)
	X₁₃	Frequency of exposure to natural disasters	The frequency of natural disasters in the village
Administrative village’s feature (X₂)	X₂₁	Village historical features	Whether the village is an old revolutionary base spot, or ethnic minorities gathering one, or border one, or not
Administrative village’s feature (X₂)	X₂₂	Population density	Population density of the village (headcount/km²)
Production and living condition (X₃)	X₃₁	Per cultivated area	Per cultivated area in the village (mu)
	X₃₂	Road access ratio	The ratio of natural villages traveling by motor vehicle roads to all natural villages in an administrative village (%)
	X₃₃	Electricity access ratio	The ratio of the households accessing to electricity to all the households in an administrative village (%)
	X₃₄	Phone access ratio	The ratio of households accessing to electricity to all the households in an administrative village (%)
		Radio and television access ratio	The ratio of households accessing to radio and television to all the households in an administrative village (%)
	X₃₅	Safe drinking water access ratio	The ratio of the households access to safe drinking water to all the households in an administrative village (%)
	X₃₆	Sanitary toilet facilities access ratio	The ratio of the households access to sanitary toilet facilities to all the households in an administrative village (%)
	X₃₇	Dangerous building ratio	The ratio of the households with dangerous buildings to all the households in an administrative village (%)
Labor force (X₄)	X₄₁	Ratio of labor force	The ratio of labor forces to all population in an administrative village
	X₄₂	Ratio of labor-out	The ratio of migrant labors to all labor forces in an administrative village
	X₄₃	Ratio of illiterate labor forces	The ratio of illiterate labors to all labor forces in an administrative village
		“Yulu Plan” participation ratio	The ratio of those labor forces participating in “Yulu Plan” to all the labor forces (%)
Medical facilities and social security (X₅)	X₅₁	Clinics per one thousand people	The clinic number that per one thousand people have in an administrative village
	X₅₂	Doctors per one thousand people	The number of doctors that per one thousand people have in an administrative village
	X₅₃	Population ratio in the New Rural Co-operative Medical Insurance of China	The ratio of population taking part in the new rural co-operative medical insurance of China to all population in an administrative village (%)
	X₅₄	Population ratio in rural social endowment insurance	The ratio of population taking part in rural social endowment insurance to all population in an administrative village (%)
Economic development (X₆)	X₆₁	Per capita net income	Per capita net income in an administrative village

Table 1

Figure 2

Figure 3

Table 2

Figure 4

Table 3

Figure 5

Table 4

Figure 6

Table 5

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Indicator	X₃₂	X₁₂	X₁₃	X₆₁	X₄₁	X₄₃	X₄₂	X₃₇	X₁₁	X₅₄
Contribution degree (%)	14.82	12.80	9.50	8.25	7.95	6.97	6.22	5.73	5.31	4.26
Average ranking	2.61	2.59	4.89	5.82	5.35	6.88	7.61	7.72	8.14	9.94
Beta	-0.220	0.164	0.168	-0.363	-0.157	-0.191	-0.158	0.116	0.038	-0.093
Indicator	X₃₆	X₂₁	X₃₁	X₃₅	X₃₄	X₂₂	X₅₃	X₃₃	X₅₂	X₅₁
Contribution degree (%)	3.41	3.35	2.51	2.35	1.83	1.53	0.89	0.87	0.84	0.60
Average ranking	10.72	11.17	12.07	13.45	13.98	15.18	17.68	17.71	17.13	19.37
Beta	-0.081	/	-0.009	-0.060	-0.086	/	-0.035	-0.041	-0.035	-0.074

Poverty type	Average VPI	Poor village ratio (%)	G-probability (%)	V-probability (%)	P-probability (%)	L-probability (%)	M-probability (%)	E-probability (%)
Single-factor dominant type	36.99	0.14	50.00	0.00	29.17	20.83	0.00	0.00
Dual-factor driving type	48.46	8.24	81.67	1.98	69.15	46.37	0.33	0.50
Three-factor dominant type	55.67	53.33	97.64	5.69	95.41	94.80	3.28	3.18
Four-factor coordinative type	56.28	28.99	98.34	33.92	96.45	98.12	32.59	40.58
Five-factor combinative type	54.34	8.36	99.40	79.17	98.00	99.23	65.43	58.78
Six-factor comprehensive type	51.58	0.94	100.00	100.00	100.00	100.00	100.00	100.00
Sum	55.08	100.00	96.63	20.59	93.71	92.09	17.64	19.36

Poverty characteristics		Geographical location differences
Poverty characteristics		Northwest of the Hu Line	Southeast of the Hu Line	T_t	T_a	T_r
Poverty status	Number of villages	6942	44519	/	/	/
Poverty status	Average VPI	57.87	52.49	0.331	0.046	0.285
Contribution degree of poverty- forming causes	Road access ratio (%)	12.78%	13.69%	0.320	0.013	0.307
	Terrain type (%)	11.39%	12.41%	0.320	0.012	0.308
	Frequency of exposure to natural disasters (%)	9.27%	9.79%	0.346	0.014	0.331
	Per capita net income (%)	8.49%	7.495	0.317	0.031	0.287
	Labor force ratio (%)	8.48%	8.50%	0.344	0.010	0.334
	Illiterate labor forces (%)	7.59%	6.86%	0.349	0.028	0.321
Poverty type	Single-factor dominant type (%)	0.05%	0.43%	2.687	0.064	2.623
	Dual-factor driving type (%)	9.48%	10.63%	0.753	0.011	0.742
	Three-factor dominant type (%)	49.64%	54.38%	0.382	0.012	0.370
	Four-factor coordinative type (%)	30.33%	25.54%	0.506	0.036	0.470
	Five-factor combinative type (%)	9.83%	8.03%	0.814	0.040	0.774
	Six-factor comprehensive type (%)	0.67%	0.99%	1.274	0.001	1.273

Poverty characteristics		Policy support differences (“Whole-village Advancement”)
Poverty characteristics		Already implemented	Being implemented	Not yet implemented	T_a	T_r	T_e
Poverty status	Number of villages	8264	19967	23230	/	/	/
Poverty status	Average VPI value	54.16	55.63	54.92	0.619	0.018	0.601
Contribution degree of poverty-forming causes	Road access ratio (%)	13.64%	14.65%	14.80%	0.651	0.014	0.636
	Terrain type (%)	13.92%	12.57%	13.07%	0.638	0.020	0.618
	Frequency of exposure to natural disasters (%)	9.84%	9.66%	9.31%	0.661	0.018	0.643
	Per capita net income (%)	7.77%	8.34%	7.84%	0.626	0.023	0.603
	Labor force ratio (%)	8.30%	7.95%	8.08%	0.663	0.019	0.644
	Ratio of illiterate labor forces (%)	6.86%	6.98%	6.85%	0.655	0.021	0.633
Poverty type	Single (%)	0.22%	0.06%	0.18%	2.728	0.021	2.706
	Two-factor driving (%)	9.97%	7.22%	8.50%	1.208	0.028	1.181
	Three-factor dominant (%)	48.71%	55.49%	53.11%	0.745	0.016	0.729
	Four-factor coordinative (%)	32.15%	27.82%	28.88%	0.852	0.022	0.830
	Five-factor combinative (%)	8.19%	8.44%	8.35%	1.304	0.022	1.282
	Six-factor comprehensive (%)	0.76%	17.31%	0.98%	1.974	0.004	1.970