Project County Selection of WFP Interventions

In Gansu Province, China

Under Country Programme 2001-2005

Han Zheng

WFP/IFAD China

Vulnerability Analysis and Mapping (VAM) Unit

July 2002

1

Background

In the WFP China Country Programme of 2001-2005, 25 counties in south of Gansu province are identified as candidate counties of WFP Gansu Intervention. This identification was agreed between WFP and MOA, based on 1) the VAM 2000 China County Analysis, which put forward 459 counties as most vulnerable and eligible for further study, and 2) the project counties suggested by the Government.

The 25 counties are located in the central and south part of Gansu, which is on the stretches of Minshan Mountains bordering the Tibetan Plateau and extends onto the Loess Plateau and Qingling Mountains. More than 80% of the total area is mountainous and suffering from frequent natural disasters.

Due to limited resource, further targeting is needed to scale down the project area and concentrate the food aid on the most needy people, therefore, further county and township targeting is required1.

General Situation of the Candidate Counties2

South of Gansu is home of quite a number of national poverty-stricken counties. The 25 candidate counties for WFP intervention are among the 43 priority counties designated by the Chinese Government for poverty reduction.

According to the data collected from the 25 counties, the total population of the 25 counties is more than 8 million, living in an area of 6 million hectares. Rural labor force accounts for about 50% of the rural population. The average mountainous area within a township is 63% and the cultivated land is only 0.14 ha per capita. Various natural disasters hit the area frequently and the agriculture is backward. The average per capita grain production in the 25 counties is less than 250 kg in latest five years and the average rural net income of 2001 is about 1100 Yuan.

The main crops of food supply are wheat, maize, tuber and some beans. Rice is rarely planted in some counties on the south border of Gansu where climatic conditions is a little better. The crop-planting pattern differs largely in different counties and the yield of each crop varies. The average wheat yield per mu is 130 kg, and the yield of maize is 200kg. The annual variability of grain is about 0.2, means the grain production is not very stable between years. In some township, this indicator can reach 0.6. Chart 1 shows the average composition of grain output in the 25 counties. Wheat production accounts for one-third of the total grain production, however, maize and potato have a proportion of about 50% of the total outputs.

1 Maps in this report are prepared by Yu Jing, VAM Assistant of WFP China. 2 All analysis of this report are based on township data provided by Gansu PMO.

2 Chart 1 – Composition of Grain Production in Candidate Counties

Composition of Grain Production

11%

7%

34%

wheat/grain maize/grain tuber/grain bean/grain 24% others/grain

24%

Husbandry is important in the life of farmers in this region. The per capita pasture land is about 1.4 mu. However, the livestock raising shows no increase in the number of goats and/or sheep, and big animals raised by each household. No trend of increase in pig raising as well. In average, each household has one draught animal, and two households have three pigs and goats and/or sheep. Chart 2 shows the situation.

Chart 2 – Livestock Raising in the Candidate Counties in Last Five Years

Livestock Raising

1.80

1.60

1.40

1.20

1997 1.00 1998 1999 2000 0.80 2001

0.60

0.40

0.20

0.00 big animal per household goat&sheep per household pig per household

3 The data of adult education show that female education is worse than that of men. Although there are quite a proportion of adult have gone to primary school, there are more than 40% of female never entered school. The primary education today is also worrying because the drop out rate is more than 10% for both boys and girls.

Chart 3 – Adult Education in Candidate Counties

Adult Education

never go to school

female adult average

primary school graduates

0.00 5.00 10.00 15.00 20.00 25.00 30.00 35.00 40.00 45.00 50.00

Infrastructure is backward in this region. About one-third of townships don’t have irrigation, while the irrigated land is about 15% of the total cultivated land in the townships that have irrigation. Only 25 percent of administrative villages have tap water, 70% of administrative villages have a village clinic.

Methodology

1. County selection criteria

It is well known that in China the intra-county disparity is huge in terms of economic development and physical conditions, thus the best way to targeting the vulnerable area at this stage is to cluster the townships and identify the most vulnerable ones. The counties with high percentage of vulnerable townships or vulnerable people should be considered for assistance, and the vulnerable townships within the county can be project townships.

2. Data and map collection

4

In accordance to the above principle, which has proved successful, township data should be collected from all the 594 townships in the 25 candidate counties. The data collection form was prepared by WFP Beijing based on the field observations of a VAM mission to that region. Based on the county data analysis and the field visit in the candidate counties, it can be seen that vulnerability of people in this areas is highly related to food production, livestock raising, income sources and social development. People in this area are living heavily on their own production, with limited supplementation of migration income. Livestock raising is popular but at a very small scale, mainly for cash income. Social infrastructure, especially road, drinking water and hospital, is backward and hinders the local development. Many people living in mountains at a high altitude above sea level don’t have much access to markets and other social facilities. Therefore, information about agriculture, income, disaster, husbandry, road, clinics and education are collected.

Gansu Provincial PMO provided all the required data to WFP Beijing on time and worked closely with the VAM Unit on data verification and revising. In the meantime, maps of the 25 counties with township boundaries and the location of administrative villages are digitized and processed in order to illustrate the final analysis results.

3. Data verification and revising

Data verification is essential to the analysis. After the data is sent to Beijing, they are imported to a MS ACCESS database for data verification. There are about 150,000 figures in the data set and the first round checking marked less than 10% skeptical or wrong data, which is much less than some other project provinces. The skeptical or wrong data are sent back to Gansu for revising and clarification. The checking-and- revising circle went twice until the data set is clean and reliable.

4. Indicator Selection

The indicator selection is in accordance with WFP definition of vulnerability, which is a composition of risk to food security and inability to cope with the risks. The higher the risk and the lower the ability to cope, the higher the vulnerability of population/area is. Social development level is also considered a part of the coping capacity. In order to obtain a comprehensive profile of the area and distinguish the vulnerable townships from the others, indicators covering demography, farming, husbandry, income, natural disaster, social infrastructure and education are calculated. By screening the indicators, only those that are available for most of the townships are studied further. For example, the indicators about natural disasters have to be excluded although they are important for that they are available for only a small number of townships. For the indicators of farming, husbandry and income, average annual growth rate and/or coefficient of variation between years are computed to show the growth trend in recent years.

Following indicators are included in further analysis.

5 Table 1 – Indicators Used in Township Vulnerability Analysis of Gansu

No. Indicators Category 1 Annual growth rate of rural population Risk (97-2001) (%) 2 Average per capita grain production Risk (kg) (97-2001) 3 Coefficient of variation of per capita Risk grain production (97-2001) 4 Per capita cultivated land (ha) (2001) Risk 5 Per capita agricultural land (ha) (2001) Risk 6 Per capita pasture land (ha) (2001) Risk 7 Per capita rural net income (yuan) Coping ability (2001) 8 Annual growth rate of rural net income Coping ability (97-2001) (%) 9 Average no. of goats & sheep per Coping ability household (98-2001) 10 Average no. of pigs per household (98- Coping ability 2001) 11 Annual growth rate of pig raising (98- Coping ability 2001) 12 Annual growth rate of goats and sheep Coping ability raising (98-2001) 13 % Of non-grain sown area/crop sown Coping ability area (2001) 14 %Village with tap water (2001) Social development 15 % Of primary dropouts (2001) Social development 16 % Of primary female dropouts (2001) Social development 17 % Adults never going to school (2001) Social development 18 % Female adults never going to school Social development (2001) 19 % Villages with complete primary Social development school (2001) 20 % Of villages with village clinics (2001) Social development 21 Dependent ratio Social development

The correlation between indicators is computed to find out the relations between indicators (Annex 1). From the correlation sheet, statistically significant correlations between certain pairs of indicators are not found. But the general relations can be seen. For instance, there is a positive relation between the average per capita grain production and average grain yield. The negative relation between per capita net income and primary dropout rate is also very reasonable. The results of correlation revealed the complexity of the causes of vulnerability in this region.

5. Methodologies of Township Analysis

6 The objective of the analysis is to cluster the 594 townships into groups with different characteristics. A programme of ADDATI, developed by the Venice University, is used. Through data standardization, correlation analysis and Principal Component Analysis (PCA), the programme clusters all the townships into a number of groups according to the manually set criteria, for example, number of clusters, number of partitions.

After PCA, active variables, which will influence the clustering results, are identified. The other indicators are regarded as supplementary variables, which help to understand the township profiles.

Table 2 – Active Variables of Clustering

No. Indicators Abbreviation in Clustering 1 Annual growth rate of rural population grpop (97-2001) (%) 2 Average per capita grain production pcgrain (kg) (97-2001) 3 Coefficient of variation of per capita cvgrain grain production (97-2001) 4 Per capita agricultural land (ha) (2001) agriland 5 Per capita rural net income (yuan) income (2001) 6 Annual growth rate of rural net income grincome (97-2001) (%) 7 Annual growth rate of pig raising (98- grphpig 2001) 8 Annual growth rate of goats and sheep grphyang raising (98-2001) 9 % Of non-grain sown area/crop sown nongrain area (2001) 10 %Village with tap water (2001) tap 11 % Of primary female dropouts (2001) fedrop 12 % Female adults never going to school femaleno (2001) 13 % Of villages with village clinics (2001) clinic

After the clustering by the programme, the profiles of each cluster are interpreted and further screening of the townships in vulnerable cluster is done. The number of vulnerable townships in each county, as well as the number of rural people living in the vulnerable townships are calculated in order to compare the concentration of vulnerable people in different counties.

Analysis result

1. Township profiling

The 594 townships of 25 candidate counties were grouped into 15 clusters. The table below shows the profiles of each cluster. From the table we consider Cluster 1, 3, 4, 5, 7, 8, 10, 13,14 could be considered for further study, because the majority

7 indicators of these clusters show a worse situation than the weighted average of all the townships. Each of the vulnerable cluster has several problems, but all of them don’t have enough food production, and the food gap can not be easily compensated by their poor income. Some of the clusters even have a poor or deteriorating husbandry, some have a high population growth pressure. The education or drinking water is worse compared with the good clusters.

Cluster 11 has very low food production, however, the income of that cluster is high and growing at a rapid speed. Cluster 6 and 12 are rich clusters with high food production, high income and good husbandry, although there are some problems with the drinking water. Cluster 2 is a large group with average situation in almost all aspects except village clinics.

Table 3 – Cluster Profiles

Cluster no. of no.of grpop Pc Cv Agri In gr grphpig gr non tap Fe Female clinic town indicator grain grain land come income phyang grain drop no ships s worse than average 1 76 7 0.73 226.5 0.16 0.56 703.6 6.35 -2.3 -2.01 20.26 9.76 32.38 36.88 37.23 2 76 6 0.19 301.8 0.16 0.8 1215 13.47 -2.1 -4.59 18.23 21.76 17.79 37.85 39.43 3 75 7 0.17 247.1 0.14 0.63 714.4 6.64 5.54 5.08 14.47 15.48 12.05 63.48 87.6 4 67 8 0.72 181.9 0.32 0.47 699.1 10.31 -1.27 -8.18 18.87 12.94 8.67 57.84 91.17 5 59 7 1.26 281.6 0.12 0.3 888.7 2.52 -9.57 -3.94 22.14 15.4 6.96 24.65 90.91 6 46 3 -0.13 354.8 0.11 0.37 1350 9.74 2.15 4.77 26.04 9.56 11.39 26.18 88.62 7 43 7 0 228.6 0.13 0.27 1118 7.37 -4.83 -5.55 20.04 79.29 6.53 47.44 89.97 8 35 8 1.43 169.0 0.33 0.38 734.2 3.04 -0.67 4.46 17.52 5.84 27.12 23.08 92.93 9 29 3 0.78 153.6 0.17 0.12 1845 11.63 -2.25 -1.82 35.36 55.85 2.84 21.53 100 10 28 9 0.85 190.0 0.24 0.59 625.2 4.11 -2.87 -2.39 9.36 81.41 34.77 79.69 32.61 11 25 5 1.3 191.6 0.36 0.17 1481 13.5 3.35 -0.68 25.16 10.92 14.88 31.23 97.08 12 16 3 1.12 437.2 0.07 0.44 2018 10.61 9.68 9.57 27.26 7.16 3.03 8.45 100 13 12 9 1.27 269.4 0.23 0.25 951.8 6.89 -22.13 -76.79 17.65 24.9 4.13 34.45 76.35 14 6 7 -0.45 304.7 0.06 27.63 638.9 3.18 -1.7 -10.26 5.18 27.8 8.36 81.82 40.67 15 1 6 0.95 209.0 0.25 0.28 1170 15.92 2.33 2.63 -823.8 9.52 1.88 45.99 85.71

Average 594 13 0.67 244.3 0.19 0.5 1062 8.15 -1.42 -2.62 19.15 23.05 13.74 37.81 80.83

The clustering results are illustrated in Map 1, on which the townships are colored according to the clusters they belong to. The color of red series represents the vulnerable clusters, while the green series stands for good ones. Some counties, such as Weiyuan, Tongwei, Zhouqu and Wen Xian, are dominated with green or blue colors, in that the townships of those counties are vulnerable compared with others. The counties, such as Li Xian, Danchang, Wudu, Lintan, Dongxiang, are in red or pink color, showing the townships in these counties are grouped into cluster 1, 3, 4, 7 and 8, which are considered vulnerable. There are some counties that red or green colors cover almost equal areas of the county, for example, Qin’an and Huining.

Within the red series, which highlighted the vulnerable townships, the characteristics of the townships are not exactly the same. In other words, different color can represent different characteristics, or tell different reasons for vulnerability. Take Dangchang county as an example. Townships in the west and east falling into cluster 4 (brown), which are poor in agricultural land, has low grain production and income. In addition, husbandry in these townships is not increasing. There are problems with

8 education and drinking water as well. The townships in the north belong to cluster 3 in pink, which is better in general than cluster 4, but adult education is worse. The other townships in yellow are quite different from the other two clusters, because the population growth rate of these townships is very high, but their income is decreasing, although the average income of these townships are not very low and the grain production is ok.

Map 1-- Clustering Results

Vulnerable Clusters in 25 Candiate Counties in Gansu

Huining

YuzhongYuzhong

JishishanJishishan DongxiangDongxiang

LinxiaLinxia GuangheGuanghe

ZhuanglangZhuanglang HezhengHezheng ZhuanglangZhuanglang TongweiTongwei

Weiyuan Weiyuan LongxiLongxi

Qin'anQin'an ZhangjiachuanZhangjiachuan

LintanLintan Gangu ZhangZhang XianXian WushanWushan

MinMin XianXian

County Boundary

DangchangDangchang LiLi XianXian Vulnerable cluster 1 (76) XiheXihe 2 (76) 3 (75) 4 (67) 5 (58) 6 (46) ZhouquZhouqu 7 (42) 8 (36) 9 (29) 10 (29) 11 (25) 12 (16) KangKang XianXian 13 (13) WuduWudu KangKang XianXian 14 (6) 15 (1) WenWen XianXian

WFP/IFAD China VAM Unit, July 2002

9

For each county, similar in-depth description of the townships can be extracted from Map 1 and Table 3. In order to underline the major causes of vulnerability in each township, a further classification based on the clustering results was done.

According to the concept of vulnerability, all the used indicators were categorized into “”Risk , “Coping Ability”and “Social development”(see Table 1). If within each category, a cluster has more than 50% of indicators falling below the overall average, it is considered that this category can be a cause of vulnerability for this cluster. As a result, the causes of vulnerability can be represented by a combination of alphas:

• R -- high risk of food insecurity • C -- low coping ability • S -- backward social development

Another letter was added to the vulnerable clusters (in grey) to represent the aspect, which is the worst, compared with other indicators of the cluster profile. The alphas are:

• f -- very low grain production • m -- very low per capita net income • h -- poor husbandry • e -- very poor education

Table 4 -- Causes of Vulnerability of Each Cluster

Cluster Risk Coping Social Overall (4 indicators) (5 indicators) development (4 indicators) 1 1 3 3 CS-m 2 0 2 3 S 3 1 3 3 CS-e 4 3 3 2 RCS-fhe 5 2 4 1 RC-h 6 1 1 1 7 2 3 1 RC-f 8 4 2 2 RS-f 9 2 1 0 10 2 4 3 RCS-fe 11 4 0 1 12 2 0 1 13 3 5 1 RC-h 14 0 5 2 CS-m 15 4 0 2 RS

10 Map 2, showing the further classification results, is a helpful tool for understanding the causes of vulnerability in each township, as well as county.

Map 2 -- Causes of Vulnerability in Vulnerable Townships

Major Causes of Vulnerability of Vulnerable Township in 25 Candidate Counties

HuiningHuining

YuzhongYuzhong

JishishanJishishan DongxiangDongxiang

LinxiaLinxia GuangheGuanghe

ZhuanglangZhuanglang HezhengHezheng ZhuanglangZhuanglang TongweiTongwei

WeiyuanWeiyuan WeiyuanWeiyuan LongxiLongxi

Qin'anQin'an ZhangjiachuanZhangjiachuan

LintanLintan GanguGangu ZhangZhang XianXian WushanWushan

MinMin XianXian

County Boundary

DangchangDangchang LiLi XianXian

Causes of Vulnerability XiheXihe CS-m (76) CS-e (75) RCS-fhe (67) RC-h (58) ZhouquZhouqu RC-f (42) RS-f (36) RCS-fe (29) RC-h (13) CS-m (6) KangKang XianXian WuduWudu KangKang XianXian

WenWen XianXian

WFP/IFAD China VAM Unit, July 2002

11

2. Proposed County and Township Selection

There are more than 400 townships falling into the vulnerable clusters for further study. Since a cluster profile represents the average situation of a cluster, while there are disparity within a cluster, strict criteria is introduced to eliminate those township that are relatively more capable to cope food insecurity than others. The criteria is 1) the average per capita grain production over 250kg; and 2) the per capita net income of 2001 is over 1000 yuan. After this screening, 197 townships in 21 candidate counties are remained in the vulnerable township list, with a total rural population around 2.6 million.

Obviously the vulnerable townships are scattering in everywhere of the region. The screening so far has successfully identified the vulnerable pockets, but yet narrowed down the project area. Therefore the next step is to find out the counties with a high percentage of vulnerable townships. Through the following table, we can identify ten counties with about 45% or above townships or rural population falling into the most vulnerable category.

The highlighted counties in the table are those have a higher percentage of the most vulnerable townships than other counties, they are: Dangchang, Dongxiang, Wushan, Li Xian, Wudu, Jishishan, Gangu, Min Xian, Lintan and Qin’an. There are three counties at the bottom of the list don’t have any townships falling into the worst situation. Those that have a low percentage of the most vulnerable townships can be considered not suitable for the interventions of food aid, but the most vulnerable townships should not be forgotten in other projects supported by either the government itself or with international assistance.

The most vulnerable townships in the above 10 counties should be considered to be included in the intervention. The list of eligible townships is in Annex.

Table 5 – Distribution of the Most Vulnerable Townships

County No.of townships No. of the most % of the most % of rural vulnerable vulnerable population townships townships living in the most vulnerable townships Dangchang 31 24 77.4 77.2 dongxiang 25 18 72.0 66.7 Wushan 20 13 65.0 57.3 Lixian 36 22 61.1 53.0 Wudu 44 26 59.1 61.5 Jishishan 18 9 50.0 47.5 Gangu 20 10 50.0 37.4 Minxian 23 11 47.8 42.2 Lintan 19 9 47.4 48.1 Qin'an 22 9 40.9 45.2 Huining 32 13 40.6 35.3 Zhouqu 22 8 36.4 33.6

12 Wenxian 25 8 32.0 34.4 Zhangjiachuan 19 6 31.6 36.9 Tongwei 23 6 26.1 25.8 Hezheng 14 3 21.4 23.7 Guanghe 10 2 20.0 13.1 Linxia 27 4 14.8 14.3 Weiyuan 20 2 10.0 6.7 Longxi 25 2 8.0 7.4 zhuanglang 23 1 4.3 4.3 Kangxian 28 1 3.6 3.5 Yuzhong 27 ------Xihe 24 ------Zhangxian 17 ------

Table 6 – Major Statistics of the Vulnerable Townships in the Ten Recommended Project Counties

Prefecture County No. of No. of No. of Total area Rural Average per Per capita Per capita vulnerable vulnerable admin. of the population capita grain agricultur net income townships households villages in vulnerable in the production al land 2001 the townships vulnerable (kg) in the (ha) of the (Yuan) of vulnerable townships vulnerable vulnerable the townships townships townships vulnerable townships Longnan Dangchang 24 50250 264 263005 211596 208 0.98 586 Longnan Lixian 22 53525 346 631349 258606 193 0.90 722 Longnan Wudu 26 70979 456 233272.3 291253 182 0.72 716 Linxia Dongxiang 18 32011 165 103393.8 170158 174 0.44 625 Linxia Jishishan 9 19056 70 30013 95250 210 0.18 594 Tianshui Wushan 13 50818 253 152627 228073 197 0.51 598 Tianshui Gangu 10 41492 203 94233 203561 146 0.39 616 Tianshui Qin’an 9 50233 213 67934 251307 160 0.12 594 Dingxi Minxian 11 38760 170 166632 171818 181 0.47 654 Gannan Lintan 9 16117 68 80675 65125 193 0.18 951

Overall 10 151 423241 2208 1823134 1946747 184 0.49 666

Location of the 25 candidate counties and the 10 proposed counties

Following maps showing the location and main indicators of the selected counties and townships. It can be seen that the 10 proposed counties are geographically continuous and the vulnerable townships within the 10 counties are not scattered. The concentration of the vulnerable townships can be helpful for the project designing and management. The ten proposed counties are in four prefectures, namely Linxia, Tianshui, Longnan and Dingxi. All the 10 proposed counties are to the south of Lanzhou, the capital of Gansu province. The southern counties are on the extent of the Qinling Mountains, and the counties such as Qin’an, Gangu and Wushan are on

13 the stretches of the Loess Plateau. The counties in Linxia prefecture are on the edge of the Tibetan Plateau and have a high proportion of minority people.

Map 3 – Location of the Candidate Counties, Proposed Counties and townships

Candidate Counties in Gansu (2002)

Heilongjiang

InnerInner MongoliaMongolia JilinJilin

10 Vulnerable Counties in Gansu Xinjiang LiaoningLiaoning Beijing Gansu TianjinTianjin Hebei Ningxia Hebei Shanxi BaiyinShiBaiyinShi Shanxi Shandong Qinghai

shaanxishaanxi Henan JiangsuJiangsu

Anhui TibetTibet Hubei Shanghai Sichuan Hubei Chongqing Chongqing ZhejiangZhejiang JiangxiJiangxi Hunan Guizhou FujianFujian

Yunnan Candiate Counties in Gansu (25) Guangxi TaiwanTaiwan Guangdong Province Boundary

Hainan YuzhongYuzhong WFP/IFAD China VAM Unit, July 2002 HuiningHuining LanzhouShiLanzhouShi JishishanJishishan DongxiangDongxiang LinxiaLinxia LinxiaLinxia linxialinxialinxia DingxiDingxi HezhengHezheng PingliangPingliang TongweiTongwei WeiyuanWeiyuan WeiyuanWeiyuan LongxiLongxi

Qin'anQin'an ZhangjiachuanZhangjiachuan LintanLintan GanguGangu ZhangZhang XianXian WushanWushan TianshuiTianshui

MinMin XianXian GannanGannan

LiLi XianXian DangchangDangchang XiheXihe LongnanLongnan ZhouquZhouqu

WuduWudu KangKang XianXian

WenWen XianXian Vulnerable Townships

County Boundary

Prefecture Boundary

name Proposed Project Counties WFP/IFAD China VAM Unit, July 2002

14

Suggestions on following steps

1. The final county selection will be determined among WFP, IFAD, MOA and local PMOs through thorough discussions. Following criteria could be considered when the final county selection is made:

Amount of resource that could be distributed in Gansu intervention. Location of the county -- The location of the counties sometimes can affect project management efficiency. Ongoing or just-completed projects assistance by other organizations

2. After the agreement on county selection is reached, the vulnerable townships in the selected counties will be included in the sampling frame of the village/household survey.

15

Table 7 -- Proposed Townships in the Proposed Counties in Gansu

Prefecture County Township code Township Total areas Rural (ha) Population Tianshui Gangu 62052305 Anyuan 12575 34740 Gangu 62052307 Jinping 3700 12644 Gangu 62052309 Xiping 10320 17494 Gangu 62052311 Kangjiatan 5770 19610 Gangu 62052312 Xiejiawan 9770 23989 Gangu 62052315 Dashi 9800 30671 Gangu 62052316 Lixin 10570 20394 Gangu 62052318 Jinchuan 10848 17956 Gangu 62052319 Wujiahe 7600 15951 Gangu 62052320 Gupo 13280 10112 Wushan 62052401 Yuanhe 6600 10178 Wushan 62052402 Mali 13867 29865 Wushan 62052403 Simen 11327 21856 Wushan 62052404 Yanghe 16267 16159 Wushan 62052405 Yan'an 12220 15288 Wushan 62052406 Tange 18733 35669 Wushan 62052407 Longtai 10460 13061 Wushan 62052408 Wenquan 6327 11746 Wushan 62052409 Caochuan 7427 6291 Wushan 62052415 Yupan 15666 15551 Wushan 62052416 Zuitou 11380 17273 Wushan 62052418 Hualin 10173 16610 Wushan 62052419 Gaolou 12180 18526 Qin'an 62052203 Liuping 5801 22666 Qin'an 62052204 Longcheng 7895 30345 Qin'an 62052205 Wuying 9033 38585 Qin'an 62052207 Lianhua 6682 33075 Qin'an 62052208 Haodi 5075 19745 Qin'an 62052209 Yebao 6722 30808 Qin'an 62052210 Anfu 7142 26571 Qin'an 62052211 Guojia 10787 33548 Qin'an 62052214 Wangpu 8797 15964 Dingxi Minxian 62242904 Xizhai 6768 16507 Minxian 62242905 Qingshui 7659 14675 Minxian 62242906 Minshan 4897 15175 Minxian 62242907 Sigou 25471 21035 Minxian 62242909 Qinxu 35722 21388 Minxian 62242910 Chabu 10506 22537 Minxian 62242916 Xiaozhai 10937 14172 Minxian 62242917 Buzi 6644 10893 Minxian 62242918 Weixin 7699 13210 Minxian 62242922 Suolong 28574 11103 Minxian 62242923 Mawu 21755 11123 Longnan Wudu 62262102 Chengjiao 15392.5 26281 Wudu 62262106 Jingping 13303.7 7821 Wudu 62262107 Puchi 11587 15573 Wudu 62262108 Shimen 6861.9 10215 Wudu 62262109 Jiaogong 8837.2 19816

16 Longnan Wudu 62262110 Majie 11757.2 27140 Wudu 62262111 Hanlin 4086.2 10424 Wudu 62262112 Bolin 6304.4 11915 Wudu 62262113 Anhuazhen 18069.3 29787 Wudu 62262114 Jinchang 11945.4 10249 Wudu 62262115 Maying 6134.2 7997 Wudu 62262116 Chiba 4295.5 5913 Wudu 62262118 Foya 6317.4 6356 Wudu 62262124 Longfeng 6317.4 11840 Wudu 62262126 Jugan 4646.7 6652 Wudu 62262127 Mobazangzuxiang 6781.2 5234 Wudu 62262128 Toufang 7003.3 6505 Wudu 62262129 Waina 11932.6 12120 Wudu 62262130 Sanhe 8651.6 11583 Wudu 62262131 Yuhuang 8021.9 8599 Wudu 62262132 Guohe 9049.5 13118 Wudu 62262137 Sancang 9570.8 10248 Wudu 62262139 Pandi 5230.3 4887 Wudu 62262140 Yuezhao 10589.3 4873 Wudu 62262141 Caohe 14235.5 3059 Wudu 62262143 Xizhi 6350.3 3048 Dangchang 62262301 A'wu 7776 11347 Dangchang 62262302 Hadapu 8085 12673 Dangchang 62262305 Nanhe 47138 7186 Dangchang 62262306 Pangjia 6714 8233 Dangchang 62262307 Bali 9955 8910 Dangchang 62262308 Lichuan 2225 12007 Dangchang 62262309 Mu'er 5546 7871 Dangchang 62262310 Dashe 5670 7098 Dangchang 62262311 Hejiabao 12177 6266 Dangchang 62262312 Dangchang 6311 9381 Dangchang 62262314 Jiahe 8490 4862 Dangchang 62262315 Jiangtai 4977 7960 Dangchang 62262316 Guan'ezangzuxiang 5683 3884 Dangchang 62262317 Chela 16299 12301 Dangchang 62262318 Xinchengzizangzuxiang 18130 6285 Dangchang 62262319 Linjiangpu 6581 6369 Dangchang 62262323 Haoti 8532 7150 Dangchang 62262324 Zhuyuan 13034 5508 Dangchang 62262326 Guanting 11254 6869 Dangchang 62262327 Qinyu 10308 8608 Dangchang 62262328 Huama 5522 6122 Dangchang 62262329 Shawan 12223 24999 Dangchang 62262330 Xinzhai 12012 13610 Dangchang 62262331 Shizi 18363 6097 Lixian 62262812 Guchengxiang 20839 13006 Lixian 62262813 Yachengxiang 18710 13008 Lixian 62262814 Luobaxiang 17780 12130 Lixian 62262815 Jiaoshanxiang 14588 13018 Lixian 62262816 Shiqiaoxiang 12530 24686 Lixian 62262817 Taopingxiang 29596 14606 Lixian 62262818 Shangpingxiang 347047 7202 Lixian 62262819 Yangpoxiang 7105 10406

17 Longnan Lixian 62262821 Jiangkouxiang 6441 10158 Lixian 62262823 Longlinxiang 12282 16307 Lixian 62262824 Zhongbaxiang 13419 15602 Lixian 62262825 Baiguanxiang 13169 13704 Lixian 62262827 Shajinxiang 20155 7960 Lixian 62262828 Baihexiang 10754 12198 Lixian 62262829 Quanshuixiang 7802 6536 Lixian 62262830 Qiaotouxiang 16249 13306 Lixian 62262831 Caopingxiang 10516 8419 Lixian 62262832 Leibaxiang 10372 10004 Lixian 62262833 Wangbaxiang 8995 11499 Lixian 62262834 Xiaoliangxiang 8612 7616 Lixian 62262835 Shanyuxiang 11718 5687 Lixian 62262836 Tanpingxiang 12670 11548 Linxia dongxiang 62292601 Suonan 4986.3 14363 dongxiang 62292602 Chuntai 7837 8690 dongxiang 62292603 Liushu 6134.5 6522 dongxiang 62292604 Dongyuan 5987.6 12632 dongxiang 62292606 Pingzhuang 3798 9493 dongxiang 62292607 Baihe 4801.2 11281 dongxiang 62292609 Nalesi 6468 17826 dongxiang 62292610 Zhaojia 3310 7485 dongxiang 62292611 Wujia 3755.5 8353 dongxiang 62292612 Guoyuan 6824 12631 dongxiang 62292613 Mianguchi 3660.5 6532 dongxiang 62292614 Yanling 3569.8 5535 dongxiang 62292615 Wangji 7924.5 9751 dongxiang 62292616 Fengshan 5514.5 5497 dongxiang 62292621 Dashu 6540.1 7672 dongxiang 62292622 Beiling 4263.3 4882 dongxiang 62292623 Longquan 11331.6 13692 dongxiang 62292624 Kaole 6687.4 7321 Jishishan 62292705 Liugouxiang 4993 11043 Jishishan 62292708 Hulinjiaxiang 5040 12410 Jishishan 62292710 Zhaizigouxiang 3433 12590 Jishishan 62292711 Jujixiang 3033 12750 Jishishan 62292712 Guoganxiang 2440 6987 Jishishan 62292713 Xuhujiaxiang 2167 9001 Jishishan 62292714 Zhongzuilingxiang 2940 10672 Jishishan 62292715 Xiaoguanxiang 2967 9539 Jishishan 62292717 Puchuanxiang 3000 10258 Gannan Lintan 62302107 Liushui 3926 8905 Lintan 62302108 Xincheng 8291 10110 Lintan 62302112 Zongzhai 6201 4920 Lintan 62302113 Sancha 8239 3572 Lintan 62302114 Longyuan 6189 4784 Lintan 62302115 Chenqi 7106 10396 Lintan 62302116 Shimen 11866 8621 Lintan 62302118 Yeliguan 15436 8568 Lintan 62302119 Bajiao 13421 5249

18 Annex 1 -- Correlation Sheet

cultivated Pasture/ hhdimen depende avepcgra cvpcgrai grruralpo %irrigate %25cul pcculland pcagrilan grpcnong pcroad %vlgcmp %vlgheal %dropou %adultno Per grincome aveinco averageg land on Hay (ha) sion ntratio in n p5 d d rainsown school th t school capita % me rainyield Slope rural net >25 Income grade (yuan) (ha) cultivated land on Slope >25 grade (ha) 1 Pasture/ Hay (ha) 0.17 1.00 hh dimension 0.10 -0.11 1.00 dependent ratio 0.03 0.03 -0.11 1.00 avepcgrain -0.02 0.04 -0.08 -0.12 1.00 cvpcgrain -0.04 -0.10 0.21 -0.07 -0.22 1.00 grruralpop5 0.03 -0.06 0.30 -0.11 -0.24 -0.14 1.00 %irrigated -0.20 -0.02 -0.12 0.15 -0.01 0.12 -0.13 1.00 %25cul 0.51 0.06 -0.15 0.07 0.09 -0.16 -0.08 -0.15 1.00 pcculland 0.41 0.10 0.03 0.00 0.23 -0.14 0.02 -0.32 0.15 1.00 pcagriland -0.02 -0.03 -0.12 -0.02 0.08 -0.12 -0.13 -0.07 0.18 0.02 1.00 grpc nongrain sown -0.04 0.13 -0.08 0.06 0.01 -0.10 -0.04 -0.01 -0.07 -0.07 0.04 1.00 pcroad -0.06 0.14 -0.14 -0.17 0.11 -0.03 -0.10 -0.04 0.01 0.12 0.15 0.03 1.00 %vlgcmp school 0.18 0.06 0.06 -0.17 0.12 0.05 0.04 0.09 -0.06 0.13 -0.09 0.00 0.05 1.00 %vlghealth 0.07 0.01 -0.11 -0.20 0.07 -0.01 0.05 0.09 -0.08 -0.07 -0.12 0.02 -0.08 0.28 1.00 %dropout 0.17 0.03 -0.04 0.15 -0.12 0.06 -0.02 -0.13 0.13 0.19 0.00 -0.09 0.01 0.14 -0.16 1.00 %adult no school -0.18 0.05 0.05 0.08 -0.12 0.03 -0.16 -0.02 -0.05 -0.26 0.10 0.03 0.08 -0.11 -0.03 0.15 1.00 Per capita rural net Income -0.01 0.07 -0.17 0.00 0.31 -0.08 -0.06 0.24 -0.10 -0.01 -0.07 0.05 0.00 0.28 0.24 -0.24 -0.24 1.00 (yuan) grincome% -0.15 0.09 -0.27 0.03 0.19 -0.01 -0.15 0.14 -0.10 -0.11 -0.07 0.09 0.09 0.02 0.08 -0.07 0.04 0.42 1.00 ave income 0.00 0.06 -0.13 0.01 0.32 -0.09 -0.06 0.24 -0.08 -0.02 -0.06 0.05 -0.03 0.28 0.24 -0.25 -0.22 0.98 0.31 1.00 average grainyield -0.32 0.00 0.05 0.00 0.38 -0.17 -0.08 0.25 -0.14 -0.27 -0.03 0.17 -0.16 0.19 0.20 -0.30 0.01 0.38 0.14 0.41 1

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