1986—2020年甘肃省六盘山片区贫困演变及启示

doi:10.12118/j.issn.1000-6060.2022.587

Abstract

Abstract:

Clarifying the long-term spatiotemporal dynamics of poverty and the related influencing factors can provide important information for the continued promotion of relative poverty and rural revitalization governance. Based on the 35 years of panel data covering the 1986—2020 period, past spatiotemporal poverty dynamics (represented by farmer’s per capita net income) of 46 counties in the Liupan Mountain area of Gansu Province were investigated, and a spatial econometric model was used to explore the related influencing factors. The results showed that: (1) Average annual growth rate of farmers’ per capita net income showed an increasing trend in 70% of the studied counties, and this trend is projected to continue in the future. (2) In 28 counties, the income growth rate experienced significant changes in 1994 and 2007, which may be due to the implementation of the “Seven-Year Priority Poverty Alleviation Program”, reform of rural taxes and administrative charges, equalization of basic public services in urban and rural areas, and rapid urbanization in Gansu Province. (3) Farmers’ per capita net income showed a strong spatiotemporal dependence, in which low-income counties showed little upward progress, and the distribution of hot and cold spots remained relatively stable. However, spatial spillover appeared to reduce path dependence. (4) In the short and long terms, per capita gross domestic product, proportion of primary industry, and urbanization rate had positive impacts on the growth of farmers’ per capita net income in local and neighboring counties. Human capital, planting structure, and transportation facilities had a significant short-term impact on farmers’ per capita net income growth locally and at a larger spatial scale, while rainfall had a long-term positive impact on farmers’ per capita net income growth, indicating the increase in temperatures and precipitation in northwest China have had important beneficial effects. The study’s findings on the spatiotemporal stability of poverty and the complex nature of the mechanisms that influence it serve to remind practitioners to consider the systematic coordination of policy measures in time and space.

Key words: poverty, spatiotemporal dynamics, driving factors, spatial panel model, Liupan Mountain Area of Gansu Province

MA Zhenbang,WANG Sijian,LI Wei,LYU Peng,GUO Xiaodong. Poverty evolution and its implications in the Liupan Mountain Area of Gansu Province from 1986 to 2020[J].Arid Land Geography, 2023, 46(9): 1545-1555.

Figures/Tables 9

Tab. 1

Model variable description"

变量	符号	指标描述及含义	样本量	均值	标准差	来源
贫困程度	income	农民人均纯收入（1986年不变价）对数/元	1610	6.12	0.62	《甘肃发展年鉴》《甘肃农村年鉴》
经济水平	perGDP	人均GDP对数/10⁴元·人^-1	1610	7.35	1.20	《甘肃发展年鉴》
城镇化水平	impervious	人均不透水地表面积的对数/m²·人^-1	1610	2.67	1.34	Gong等^[19]
产业结构	GDP_1st	GDP中第一产业占比/%	1610	0.35	0.19	《甘肃发展年鉴》
种植结构	cashcrop	农作物播种面积中经济作物占比/%	1610	0.25	0.10	《甘肃发展年鉴》《甘肃农村年鉴》
农业机械化	machine	单位耕地农业机械总动力的对数/kW·hm^-2	1610	0.94	1.01	《甘肃发展年鉴》《甘肃农村年鉴》
农业技术进步	fertilizer	单位耕地化肥施用折纯量的对数/kg·hm^-2	1610	4.82	0.69	《甘肃发展年鉴》《甘肃农村年鉴》
财政支持力度	fiscal_ER	财政支出与财政收入之比的对数	1610	1.69	0.91	《甘肃发展年鉴》《甘肃农村年鉴》
年降水量	rainfall	年降水量的对数/mm	1610	6.18	0.27	彭守璋^[18]
灌溉设施	irrigation	有效灌溉面积占耕地比例/%	1610	0.21	0.19	《甘肃农村年鉴》
高等级交通	rail&highway	是否通铁路或高速公路	1610	0.42	0.49	《甘肃路谱》，中国科学院资源环境科学与数据中心，1:25000全国基础地理数据库
公路密度	road_den	单位面积国省县道里程的对数/km·km^-2	1610	-0.78	0.56	《甘肃路谱》，中国科学院资源环境科学与数据中心，1:250000全国基础地理数据库
家庭规模	household	户均人口数量的对数/人	1610	1.45	0.16	《甘肃发展年鉴》《甘肃农村年鉴》
教育水平	edu_year	人均受教育年限的对数/a	1610	1.80	0.35	人口普查数据（三普到七普），人口抽样调查数据（1987、1996、2005年和2015年）

Tab. 1

Fig. 1

Tab. 2

Fig. 2

Fig. 3

Fig. 4

Tab. 3

Tab. 4

Estimation results of panel data model"

模型变量		pols	xtreg	xtabond2	queen	que_lag	iver	iver2	knn5
模型变量		模型（1）	模型（2）	模型（3）	模型（4）	模型（5）	模型（6）	模型（7）	模型（8）
自变量	L_income	-	-	0.879^***	-	0.574^***	0.594^***	0.584^***	0.586^***
	S_income	-	-	-	0.462^***	0.343^***	0.612^***	0.441^***	0.324^***
	perGDP	0.425^***	0.387^***	0.066^**	0.065^***	0.047^***	0.054^***	0.045^***	0.044^***
	impervious	0.007	0.134^***	0.000	0.014^***	0.006	0.008	0.006	0.005
	GDP_1st	0.775^***	0.497^***	0.129^*	0.154^***	0.107^***	0.147^***	0.098^***	0.096^**
	cashcrop	0.316^***	0.204^*	0.038	0.013	-0.022	-0.013	-0.026	-0.016
	machine	0.093^***	0.120^**	-0.021	0.009	0.002	-0.001	0.000	0.000
	fertilizer	-0.015	-0.051^***	-0.002	0.002	-0.003	-0.004	0.000	-0.004
	fiscal_ER	0.070^***	0.058^***	0.021^***	-0.005	-0.002	-0.005	-0.006	-0.005
	rainfall	0.093^***	-0.014	0.009	0.099^**	0.062	0.018	0.058	0.055
	irrigation	-0.103^*	0.331^***	0.066	0.139^***	0.047	0.029	0.095^***	0.094^***
	rail&highway	0.067^***	0.010^***	0.007	0.007	-0.001	0.005	-0.003	-0.002
	road_den	0.115^***	0.061	0.018^*	0.004	0.002	0.003	-0.004	0.000
	household	0.379^***	0.344^***	0.083	-0.003	0.010	0.030	0.009	0.010
	edu_year	0.329^***	0.088	0.150^***	0.002	0.013	0.005	0.010	0.009
	_cons	0.854^***	2.089^***	-0.218	-	-	-	-	-
空间滞后自变量	perGDP	-	-	-	0.054^***	0.013	0.324^***	0.007	0.001
空间滞后自变量	impervious	-	-	-	0.045^***	0.022^**	0.105^*	0.050^***	-0.009
	GDP_1st	-	-	-	0.160^***	0.075^**	0.160^***	0.195^***	0.123^***
	cashcrop	-	-	-	-0.032	0.016	0.152^***	0.105	0.040
	machine	-	-	-	-0.016^*	-0.009	0.049	-0.008	0.012
	fertilizer	-	-	-	0.023^*	-0.016	-0.035	-0.009	-0.013
	fiscal_ER	-	-	-	0.004	-0.008	-0.013	-0.018	-0.013
	rainfall	-	-	-	-0.148^***	-0.050	-0.082	-0.062	-0.045
	irrigation	-	-	-	0.041	-0.026	0.191	-0.118^*	-0.047
	rail&highway	-	-	-	-0.015	-0.015	0.0131^***	-0.013	-0.013
	road_den	-	-	-	-0.073^***	-0.012	0.047^***	0.058^*	0.008
	household	-	-	-	0.096	0.066	0.185^***	0.191^**	0.091
	edu_year	-	-	-	0.091^***	0.030	0.278^**	0.033	0.036
	N	1610	1610	1564	1610	1564	1564	1564	1564
	R²	0.913	0.934	-	0.771	0.971	0.887	0.969	0.979
	AIC	-874.842	-1522.114	-	-3991.086	-4706.726	-4605.177	-4658.896	-4659.206
	Log-likelihood	451.421	774.057	-	2049.543	2434.363	2383.588	2410.448	2410.603

Tab. 4

Tab. 5

Decomposition of the spatial spillover effect of driving factors for farmers’ per capita net income"

效应	变量	短期					长期
		queen	que_lag	iver	iver2	knn5	queen	que_lag	iver	iver2	knn5
		模型（4）	模型（5）	模型（6）	模型（7）	模型（8）	模型（4）	模型（5）	模型（6）	模型（7）	模型（8）
直接效应	perGDP	-	0.050^***	0.070^***	0.046^***	0.044^***	0.079^***	0.177^***	0.118^**	0.280	0.134^***
	impervious	-	0.009^*	0.001^*	0.009^*	0.005	0.023^***	0.051^**	0.015	0.172	0.009
	GDP_1st	-	0.118^***	0.225^***	0.111^***	0.105^***	0.192^***	0.462^***	0.293	1.312	0.394^***
	cashcrop	-	-0.020	0.059^**	-0.018	-0.011	0.008	-0.053	-0.081	0.157	-0.009
	machine	-	0.001	0.002	0.000	0.001	0.007	-0.006	-0.003	0.023	0.011
	fertilizer	-	-0.005	-0.006	-0.001	-0.005	0.006	-0.033	-0.008	-0.087	-0.024
	fiscal_ER	-	-0.004	-0.006	-0.007	-0.006	-0.004	-0.020	-0.011	-0.120	-0.027
	rainfall	-	0.060^*	0.016	0.057^*	0.055	0.086^*	0.151^*	0.050	0.043	0.136
	irrigation	-	0.046	0.039	0.090^***	0.093^***	0.161^***	0.133	0.060	-0.081	0.255^**
	rail&highway	-	-0.003	0.011^*	-0.004	-0.003	0.005	-0.025	0.006	-0.124	-0.017
	road_den	-	0.002	0.003^**	0.001	0.001	-0.008	-0.006	-0.008	0.154	0.009
	household	-	0.021	0.012^***	0.024	0.019	0.011	0.133	0.010	0.562	0.117
	edu_year	-	0.016	0.017^*	0.012	0.011	0.018	0.080	0.000	0.102	0.054
间接效应	perGDP	-	0.040^**	0.092^***	0.044	0.020	0.144^***	0.595^**	-2.107	6.443	0.392
	impervious	-	0.033^**	0.029^*	0.091^***	-0.009	0.087^***	0.313^*	-0.629	5.681	-0.056
	GDP_1st	-	0.155^***	0.436^***	0.405^***	0.213^***	0.393^***	1.860^**	-9.562	41.060	2.211^**
	cashcrop	-	0.007	0.390^***	0.157	0.044	-0.044	-0.044	-7.823	7.981	0.313
	machine	-	-0.012	0.125	-0.014	0.016	-0.019	-0.087	-0.237	1.246	0.140
	fertilizer	-	-0.025^*	-0.096	-0.016	-0.020	0.039^*	-0.227	0.213	-3.657	-0.186
	fiscal_ER	-	-0.013	-0.042	-0.036	-0.021	0.003	-0.123	0.117	-4.161	-0.198
	rainfall	-	-0.042	-0.191	-0.067	-0.041	-0.177^***	0.014	0.298	-4.340	-0.013
	irrigation	-	-0.004	0.575	-0.121	-0.015	0.180	0.202	-1.344	-13.290	0.381
	rail&highway	-	-0.022	0.035^**	-0.026	-0.019	-0.021	-0.191	-0.717	-4.797	-0.167
	road_den	-	-0.015	0.123^***	0.099^**	0.013	-0.120^***	-0.107	-2.514	5.963	0.106
	household	-	0.098	0.486^***	0.336^**	0.133	0.154^*	0.898	-10.030	18.680	1.159
	edu_year	-	0.046	0.070^**	0.060	0.051	0.154^***	0.450	-1.432	2.537	0.463

Tab. 5

References 29

[1]	中华人民共和国国务院新闻办公室. 人类减贫的中国实践[R]. 北京: 中华人民共和国国务院新闻办公室, 2021.
	[Information Office of the State Council of the People’s Republic of China. Poverty alleviation: China’s experience and contribution[R]. Beijing: Information Office of the State Council of the People’s Republic of China, 2021.]
[2]	樊杰, 周侃, 伍健雄. 中国相对贫困地区可持续发展问题典型研究与政策前瞻[J]. 中国科学院院刊, 2020, 35(10): 1249-1263.
	[Fan Jie, Zhou Kan, Wu Jianxiong. Typical study on sustainable development in relative poverty areas and policy outlook of China[J]. Bulletin of Chinese Academy of Sciences, 2020, 35(10): 1249-1263.]
[3]	樊增增, 邹薇. 从脱贫攻坚走向共同富裕: 中国相对贫困的动态识别与贫困变化的量化分解[J]. 中国工业经济, 2021(10): 59-77.
	[Fan Zengzeng, Zou Wei. From anti-poverty campaign to common prosperity: Dynamic identification of relative poverty and quantitative decomposition of poverty changes in China[J]. China Industrial Economics, 2021(10): 59-77.]
[4]	罗蓉, 何黄琪, 陈爽. 原连片特困地区共同富裕能力评价及其演变跃迁[J]. 经济地理, 2022, 42(8): 154-164.
	[Luo Rong, He Huangqi, Chen Shuang. Original contiguous destitute areas’ common prosperity capability evaluation and evolution transition[J]. Economic Geography, 2022, 42(8): 154-164.]
[5]	严小燕, 祁新华. 贫困动态的测度方法与演化机制[J]. 地理学报, 2021, 76(10): 2425-2438. doi: 10.11821/dlxb202110007
	[Yan Xiaoyan, Qi Xinhua. The measurement method and evolution mechanism of poverty dynamics[J]. Acta Geographica Sinica, 2021, 76(10): 2425-2438.] doi: 10.11821/dlxb202110007
[6]	李雨欣, 薛东前, 马蓓蓓, 等. 黄土高原地区农村贫困空间演化及偏远特征[J]. 干旱区地理, 2021, 44(2): 534-543.
	[Li Yuxin, Xue Dongqian, Ma Beibei, et al. Spatial evolution and remote characteristics of rural poverty in the Loess Plateau, China[J]. Arid Land Geography, 2021, 44(2): 534-543.]
[7]	周侃, 王传胜. 中国贫困地区时空格局与差别化脱贫政策研究[J]. 中国科学院院刊, 2016, 31(1): 101-111.
	[Zhou Kan, Wang Chuansheng. Spatial-temporal pattern of poverty-stricken areas and its differential policies for poverty alleviation in China[J]. Bulletin of Chinese Academy of Sciences, 2016, 31(1): 101-111.]
[8]	Luo X, Jin G, Wan Q, et al. Spatial poverty traps in rural China: Aggregation, persistence, and reinforcement[J]. Area, 2021, 53(1): 56-66. doi: 10.1111/area.v53.1
[9]	王永明, 王美霞, 吴殿廷, 等. 贵州省乡村贫困空间格局与形成机制分析[J]. 地理科学, 2017, 37(2): 217-227. doi: 10.13249/j.cnki.sgs.2017.02.007
	[Wang Yongming, Wang Meixia, Wu Dianting, et al. Spatial patterns and determinants of rural poverty: A case of Guizhou Province, China[J]. Scientia Geographica Sinica, 2017, 37(2): 217-227.] doi: 10.13249/j.cnki.sgs.2017.02.007
[10]	罗庆, 樊新生, 高更和, 等. 秦巴山区贫困村的空间分布特征及其影响因素[J]. 经济地理, 2016, 36(4): 126-132.
	[Luo Qing, Fan Xinsheng, Gao Genghe, et al. Spatial distribution of poverty village and influencing factors in Qinba Mountains[J]. Economic Geography, 2016, 36(4): 126-132.]
[11]	王永明, 王美霞. 武陵山片区县域相对贫困的时空演化与驱动因素[J]. 山地学报, 2021, 39(4): 576-586.
	[Wang Yongming, Wang Meixia. The temporal spatial evolution and driving fact of relative poverty in the Wuling Mountainous Area, China[J]. Mountain Research, 2021, 39(4): 576-586.]
[12]	马振邦, 贾卓, 常跟应, 等. 区域农村贫困格局的形成: 空间依赖、空间分异与尺度效应[J]. 经济地理, 2022, 42(3): 210-221.
	[Ma Zhenbang, Jia Zhuo, Chang Genying, et al. Spatial dependence, spatial variation and scale effect in the formation of rural poverty pattern[J]. Economic Geography, 2022, 42(3): 210-221.]
[13]	兰海霞, 赵雪雁. 基于面板门槛模型的中国旅游发展减贫效应研究[J]. 干旱区地理, 2020, 43(1): 248-259.
	[Lan Haixia, Zhao Xueyan. Poverty alleviation effect of China’s tourism development based on panel threshold model[J]. Arid Land Geography, 2020, 43(1): 248-259.]
[14]	王正谱. 集中支持重点帮扶县, 不仅要补短板, 更要帮发展——在国家乡村振兴重点帮扶县工作会议上的讲话[J]. 乡村振兴, 2021(10): 34-37.
	[Wang Zhengpu. Focus on the key assistance counties, not only to make up the shortage, but also to promote development: Speech at the working conference on national key assistance counties[J]. Rural Revitalization, 2021(10): 34-37.]
[15]	何仁伟, 樊杰, 李光勤. 环京津贫困带的时空演变与形成机理[J]. 经济地理, 2018, 38(6): 1-9. doi: 10.2307/142321
	[He Renwei, Fan Jie, Li Guangqin. Spatiotemporal evolution and formation mechanism of the poverty belt around Beijing and Tianjin[J]. Economic Geography, 2018, 38(6): 1-9.] doi: 10.2307/142321
[16]	刘小鹏, 李伟华, 王鹏, 等. 发展地理学视角下欠发达地区贫困的地方分异与治理[J]. 地理学报, 2019, 74(10): 2108-2122. doi: 10.11821/dlxb201910011
	[Liu Xiaopeng, Li Weihua, Wang Peng, et al. Local differentiation and alleviation of poverty in underdeveloped areas based on development geography[J]. Acta Geographica Sinica, 2019, 74(10): 2108-2122.] doi: 10.11821/dlxb201910011
[17]	丁建军, 宁燕. 湖南武陵山片区农民人均纯收入多样性特征及其对贫困的影响[J]. 地理科学, 2016, 36(7): 1027-1035. doi: 10.13249/j.cnki.sgs.2016.07.008
	[Ding Jianjun, Ning Yan. The characteristics and influence on poverty of farmers’ income diversity in Wuling Mountain Area of Hunan Province[J]. Scientia Geographica Sinica, 2016, 36(7): 1027-1035.] doi: 10.13249/j.cnki.sgs.2016.07.008
[18]	彭守璋. 中国1 km分辨率逐月降水量数据集(1901—2021)[EB/OL]. [2022-10-08]. http://poles.tpdc.ac.cn/zh-hans/data/faae7605-a0f2-4d18-b28f-5cee413766a2/.
	[Peng Shouzhang. 1-km monthly precipitation dataset for China (1901—2021)[EB/OL]. [2022-10-08]. http://poles.tpdc.ac.cn/zh-hans/data/faae7605-a0f2-4d18-b28f-5cee413766a2/.]
[19]	Gong P, Li X C, Wang J, et al. Annual maps of global artificial impervious area (GAIA) between 1985 and 2018[J]. Remote Sensing of Environment, 2020, 236: 111510, doi: 10.1016/j.rse.2019.111510.
[20]	梁大林, 唐海萍. 青藏高原两种高寒草地植被变化及其水温驱动因素分析[J]. 生态学报, 2022, 42(1): 287-300.
	[Liang Dalin, Tang Haiping. Analysis of vegetation changes and water temperature driving factors in two alpine grasslands on the Qinghai-Tibet Plateau[J]. Acta Ecologica Sinica, 2022, 42(1): 287-300.]
[21]	Weron R. Estimating long-range dependence: Finite sample properties and confidence intervals[J]. Physica A: Statistical Mechanics and its Applications, 2002, 312(1-2): 285-299. doi: 10.1016/S0378-4371(02)00961-5
[22]	Pettitt Anthony N. A non-parametric approach to the change-point problem[J]. Journal of the Royal Statistical Society: Series C, 1979, 28(2): 126-135.
[23]	陈培阳, 朱喜钢. 中国区域经济趋同: 基于县级尺度的空间马尔可夫链分析[J]. 地理科学, 2013, 33(11): 1302-1308. doi: 10.13249/j.cnki.sgs.2013.011.1302
	[Chen Peiyang, Zhu Xigang. Regional convergence at county level in China[J]. Scientia Geographica Sinica, 2013, 33(11): 1302-1308.] doi: 10.13249/j.cnki.sgs.2013.011.1302
[24]	Elhorst P J, Zandberg E, De Haan J. The impact of interaction effects among neighbouring countries on financial liberalization and reform: A dynamic spatial panel data approach[J]. Spatial Economic Analysis, 2013, 8(3): 293-313. doi: 10.1080/17421772.2012.760136
[25]	Lesage J, Pace R K. Introduction to spatial econometrics[M]. Virginia Beach: Chapman and Hall/CRC Press, 2009: 25-42.
[26]	李子奈, 潘文卿. 计量经济学[M]. 第三版. 北京: 高等教育出版社, 2010: 295-300.
	[Li Zinai, Pan Wenqing. Econometrics[M]. 3^rd ed. Beijing: Higher Education Press, 2010: 295-300.]
[27]	丁志国, 赵宣凯, 赵晶. 直接影响与空间溢出效应: 我国城市化进程对城乡收入差距的影响路径识别[J]. 数量经济技术经济研究, 2011, 28(9): 118-130.
	[Ding Zhiguo, Zhao Xuankai, Zhao Jing. Direct and spillover effects: How does the urbanization in China affect the urban-rural income inequality[J]. Journal of Quantitative & Technological Economics, 2011, 28(9): 118-130.]
[28]	王艺明, 刘志红. 大型公共支出项目的政策效果评估——以“八七扶贫攻坚计划”为例[J]. 财贸经济, 2016, 37(1): 33-47.
	[Wang Yiming, Liu Zhihong. Evaluating the policy effect of large-scale public expenditure projects: Taking “Seven-Year Priority Poverty Alleviation Program” as an example[J]. Finance & Trade Economics, 2016, 37(1): 33-47.]
[29]	李明, 孙洪泉, 苏志诚. 中国西北气候干湿变化研究进展[J]. 地理研究, 2021, 40(4): 1180-1194. doi: 10.11821/dlyj020200328
	[Li Ming, Sun Hongquan, Su Zhicheng. Research progress in dry/wet climate variation in northwest China[J]. Geographical Research, 2021, 40(4): 1180-1194.] doi: 10.11821/dlyj020200328

县区	年均增速/%	Theil-Sen指数	Hurst指数
全部46个县区	6.04 （4.91~7.42）	0.22 （0.07~0.40）	0.64 （0.55~0.75）
榆中县，临夏县，麦积区，庄浪县，临洮县，静宁县，广河县，和政县，东乡县，皋兰县，永登县	5.64 （4.91~6.12）	0.30^*** （0.24~0.40）	0.64 （0.54~0.73）
镇原县，崇信县，安定区，灵台县，秦州区，秦安县，宁县，泾川县，康乐县，会宁县，景泰县，临夏市	5.97 （5.44~6.45）	0.22^** （0.18~0.25）	0.64 （0.56~0.73）
平川区，武山县，甘谷县，古浪县，合水县，环县，庆城县，靖远县，永靖县	6.26 （5.50~7.12）	0.21^* （0.16~0.29）	0.63 （0.58~0.72）
积石山县，通渭县，陇西县，渭源县，张家川回族自治县，漳县，清水县，华池县，华亭市，正宁县，西峰区，白银区，崆峒区，岷县	6.26 （5.65~7.42）	0.16 （0.07~0.24）	0.64 （0.56~0.75）

矩阵	人均纯收入水平		人均纯收入水平
矩阵	人均纯收入水平		高	中高	中低	低
马尔科夫矩阵		高	90.2	8.6	0.7	0.5
		中高	9.9	78.7	10.7	0.8
		中低	0.8	10.7	77.1	11.4
		低	0.0	1.2	10.5	88.4
空间马尔科夫矩阵	高	高	90.9	8.7	0.4	0.0
		中高	15.7	80.0	4.4	0.0
		中低	0.0	19.4	71.0	9.7
		低	0.0	0.0	25.0	75.0
	中高	高	91.2	8.8	0.0	0.0
		中高	9.6	85.2	5.2	0.0
		中低	1.9	14.8	74.1	9.3
		低	0.0	0.0	12.8	87.2
	中低	高	50.0	33.3	16.7	0.0
		中高	1.5	81.8	16.7	0.0
		中低	0.0	9.5	81.0	9.5
		低	0.0	1.0	12.4	86.7
	低	高	100.0	0.0	0.0	0.0
		中高	0.0	78.1	18.8	3.1
		中低	0.0	4.6	87.0	8.3
		低	0.0	0.0	5.9	94.1

Poverty evolution and its implications in the Liupan Mountain Area of Gansu Province from 1986 to 2020

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