Causes of Regional Poverty in Xinjiang, China Chai Jun,1 Cheng Jing, Cheng Qun Ling, Jiang Zhi Qin2

Introduction Over recent decades, all levels of government in China have invested substantial resources to accelerate the economic development of poverty-stricken regions – and achieved remarkable success. However, the deepening of market-oriented reform together with the impact of the '87' Poverty Relief Program have changed the landscape for poverty alleviation in China. This paper attempts to take account of the changing nature of poverty alleviation work. This is problematic because poverty arises from a large range factors. Furthermore, the causes of poverty can be region-specific. Given these realities, the effective targeting of poverty alleviation programs needs to take into account regional variations. As an attempt to take into account regional variations and differing causes of poverty, this paper identifies factors that affect the sustainable social and economic development of 30 poverty-stricken counties in the Xinjiang Uygur Autonomous Region. Data on available statistical indicators were collected by the survey team from the poverty alleviation offices of 30 counties. This data was then organized and subject to principal component analysis. This enabled the identification of the key factors that affect development in each county and to make recommendations about how to target future poverty alleviation funding to appropriate activities in different counties. Methods used to collect and analyse data, as well as the results of the analysis, will be of interest to those investigating development and poverty alleviation.

1

Mr Chai Jun is a Visiting Scholar in the School of Natural and Rural Systems Management,

The University of Queensland. Thanks to Colin Brown and Scott Waldron for comments on the paper. 2

All authors, including Mr Chai Jun, are from The School of Economics and Management of

Xinjiang Agriculture University of China. 1

1 The system of indicators in analysing The system of indicators in this paper is based on the research results of the Sustainable Social and Economic Development Group within the Chinese Academy of Science. Analysis is built on the actual situation facing selected poverty-stricken regions and the data on available statistical indicators. Table 1 presents a hierarchy of indicators used to evaluate poverty conditions. The detailed poverty indicators in the right hand column can be aggregated into the larger sub-categories (or variables) in the middle column. In turn, these sub-categories form the major categories of poverty indicators in the right hand column. This hierarchy forms the basis of the principal component analysis explained in more detail below. Table 1

Indicator system of evaluation

Survival Support System Z1

1.The average per capita land area (Z111) 2.The output rate of land (Z112) 3.The average per capita cultivated land area (Z113) 4.The average per capita plough area (Z114) Status of survival resources 5.The quantity of average per capita water Z11 resource (Z115) 6.The quantity of average per capita available water resource (Z116) 7. The area of drought , flooding and salinity and alkalinity in early 2002 (Z117) 1.The total power of agricultural machinery on average per sown area (Z121) 2.The electricity consumed on average Level of agricultural inputs sown area (Z122) Z12 3.Effective irrigated area (Z123) 4.The producing expenditure on average household size (Z124) 5.The agricultural budgetary expenditure on average sown area (Z125) 1. The yield of grain per hectare (Z131) 2. The productivity of agricultural labour (Z132) 3. Per capita agricultural output value (Z133) Translated efficiency of resource 4. Per sown area agricultural output Z13 value (Z134) 5. The increase ratio of agricultural output value (Z135) 6. Per capita annual net income of rural households (Z136)

2

Power of survival sustainment Z14

Development Support System Z2

Environmental Support System Z3

Social Support System Z4

Level of regional development Z21

Level of regional ecosystem Z31 Power of ecosystem resisting counter-reaction Z32

Level of social development Z41

Level of social security Z42

3

1. The fluctuating coefficient of agricultural out value (Z141) 2. The fluctuating coefficient of grain output (Z142) 3. The fluctuating coefficient of per capita annual net income of rural households (Z143) 4. The proportion of high yield land (Z144) 1. Number of telephones per 1000 capita (Z211) 2. Percentage of administrative village access to highway (Z212) 3. The supply of electric power in administrative village (Z213) 4. The supply of drink water (Z214) 5. Per capita GDP (Z215) 6. The growth rate of GDP (216) 7. The growth rate of investment in fixed assets (Z217) 8. Per capita savings deposit in urban and rural in the end of 2002 (Z218) 9. The ratio of non-agricultural industry in GDP (Z219) 10. The ratio of livestock industry in agricultural industry (Z210) 1. The ratio of desertification (Z311) 2. The ratio of salinization and alkalinization (Z312) 1. The ratio of forest belt (Z321) 2. The area of tree cultivation (Z322) 1. Natural growth rate of population (411) 2. The percentage of illiteracy (Z412) 3. The percentage of labour in the tertiary industry (Z413) 4. The ratio of urbanization (Z414) 5. The population of per households (Z415) 1. The poverty occurring proportion (Z421) 2. The percentage returning proportion (422) 3. The percentage of young labour (Z423) 4. The percentage of middle age labour (Z424) 5. The percentage of labour over 55 years of age (Z425)

Level of social advancement Z43

Power of regional education Z51

Knowledge Support System Z5

Power of regional science and technology Z52

Power of regional management Z53

1. The percentage of illiteracy of labour (Z431) 2. The percentage of primary level labour (Z432) 3. The percentage of junior high school level labour (Z433) 4. The percentage of senior high school level labour (Z434) 5. The percentage of college and higher level labour (Z435) 1. The percentage of educational funds in GDP (Z511) 2. Student–teacher ratio in primary school (512) 3. Student–teacher ratio in junior high school (513) 4. Student–teacher ratio in senior high school (514) 5. The ratio of enrolment of school-age children (515) 1. The number of scientific and technical personnel in agriculture per 1000 person (521) 2. The proportion of scientific and technical personnel in agriculture from specialized secondary and higher education (522) 1. The proportion of budgetary revenue and expenditure (Z531) 2. The fluctuating coefficient of economical development (Z532) 3. The proportion of expenditure for administration in budgetary expenditure (Z534) 4. Per capita expenditure supplied by themselves in rural household (Z535) 5. Per capita supplied by market in rural household (Z536)

2 Data and the method of analysis The research units in this paper are the poverty-stricken counties in the Xinjiang Uygur Autonomous Region. In the investigation, 30 key counties were selected and data on available statistical indicators collated by the survey team from the poverty alleviation offices of 30 counties. Data collation was based on the system of indicators outlined in Table 1. In particular, data was collected on items in the right hand column of Table 1. To obtain the key factors that affect development in each county, this data was organized and subject to principal component analysis. The analysis has three main steps. Firstly, the primary data is standardized. Secondly, the integrated function is specified as: 4

Zij= α 1 Fij1+ α 2 Fij2+…+ α m Fijm In the integrated function form, F is the principal component and it is decided by the size of the containing information. The term α is denoted as the weighted data dependent on the contribution of each principal component to variance. Thirdly, each county is placed in rank order based on Zij in different levels. This form of analysis enables us to derive values on major poverty indicators, in particular the major categories on the left hand and middle columns of Table 1.

3 Results This section summarises the results of analysis with reference to the major poverty categories in the left hand column of Table 1. 3.1 Survival Support System（Z1） The relevant variable produces three principal components: F11, F12 and F13. F11=-0.337Z11+0.881Z12-0.097Z13+0.338Z14 F12=0.302Z11+0.342Z12-0.042Z13+0.896Z14 F13=0.036Z11-0.065Z12+1.011Z13-0.043Z14 The integrated function of Survival Support System (Z1) is: Z1=（0.501/0.916） F11 +（0.258/0.916）F12 +（0.157/0.916）F13 The value derived for Z1 shows us the basic situation of each county with regard to the Survival Support System. Based on the results of the analysis, the counties included in the study are then ranked against each other. The results and the rankings are listed in Table 2. Table 2 Ranking of each county in Survival Support System Prefecture/County Hotan Prefecture Moyu County

F11 Ranking F12 17.88 0.25 27 1.18

Ranking F13 Ranking Z1 Ranking 13.75 24 19.75 12 0.68 18 1.32 5

Pishan County Yutian County

0.49 0.62

20 13

0.94 1.08

19 16

0.31 0.43

30 26

0.64 0.8

25 18

Lop County Hotan County

0.4 0.06

24 29

0.86 1.31

23 7

0.54 0.46

19 24

0.78 1.53

20 3

Minfeng County Qira County

1.74 0.39

1 26

1.6 0.72

4 28

0.44 0.53

25 21

0.5 0.5

29 28

Hotan City

1.05

3

2.11

1

0.33

29

0.48

30

0.45

15.11 22

1.11

11.89 15

1.5

12.22 5

0.74

9.56 22

Kashi Prefecture Taxkorgan County

5

Yengisar County Shufu

0.62 0.92

14 6

1.15 1.43

14 6

0.78 1.17

14 7

1.12 1.72

7 1

Jiashi County Yopurga County

0.58 0.8

17 9

1.05 0.91

18 21

0.94 0.93

10 11

1.01 0.79

12 19

Shule County Shache County

0.41 0.03

23 30

1.3 1.27

8 9

0.71 0.95

17 9

1.09 1.7

8 2

0.99 0.7

4 11

1.77 1.18

3 13

0.53 0.76

22 15

1.33 1.05

4 11

0.52 0.89

14.25 19 7

0.85 1.81

18.25 24 2

0.47 0.76

18 23 16

0.6 1

20.25 27 14

0.74 0.46

10 21

0.71 1.06

30 17

1.36 0.42

6 27

0.73 0.81

23 17

Aksu Prefecture Kalpin County

0.6

15.5 15

0.71

20 29

1.05

14 8

0.62

21 26

Wushi County

0.59

16

1.21

11

0.54

20

0.91

16

0.69

12 12

1.23

10 10

0.39

28 28

1.06

10 10

0.55 0.4

21.5 18 25

0.94 0.83

22.5 20 25

2.08 0.79

7 1 13

1.26 0.75

13.6 6 21

0.95

3.5 5

0.83

26.5 26

0.85

7 12

0.66

18.5 24

1.07

2

0.8

27

1.75

2

1.01

13

0.81 0.11

18 8 28

1.51 0.88

13.5 5 22

1.69 1.51

3.5 3 4

1.09 1

12 9 15

Bachu County Yecheng County Kirgiz Autonomous Prefecture of Kizilsu Akto County Wuqia County Akqi County Artux City

Hami Prefecture Barkol County Tacheng Prefecture Hoboksar County Toli County Altay Prefecture Qinghe County Jeminay County Ili Prefecture Nilk County Qapqal County

3.2 Development Support System（Z2） Because there is only one variable in the system, we can order according to the value of the variable. The results are listed in Table 3. Table 3 Ranking of each county in Development Support System Prefecture/County

Z2

Ranking

0.5

15.38 19

Pishan County Yutian County

0.55 0.59

14 12

Lop County Hotan County

0.41 0.42

27 25

Hotan Prefecture Moyu County

6

Minfeng County Qira County

1.09 0.48

4 21

Hotan City

1.93

1

1.01 0.52

15.11 5 16

Shufu Jiashi County

0.4 0.52

28 18

Yopurga County Shule County

0.49 0.78

20 7

Shache County Bachu County

0.45 0.89

23 6

0.57

13

Kashi Prefecture Taxkorgan County Yengisar County

Yecheng County Kirgiz Autonomous Prefecture of Kizilsu Akto County

0.53

15

Wuqia County Akqi County

1.36 0.42

2 26

0.61

11

0.52 0.69

13 17 9

0.43

24 24

0.63

16 10

0.48

22

0.39 0.31

29.5 29 30

0.72

5.5 8

1.2

3

13.5

Artux City Aksu Prefecture Kalpin County Wushi County Hami Prefecture Barkol County Tacheng Prefecture Hoboksar County Toli County Altay Prefecture Qinghe County Jeminay County Ili Prefecture Qapqal County Nilk County

3.3 Environment Support System（Z3） The integrated function of Environment Support System is: Z3=0.587Z31+0.413Z32 The value of Z3 can show us the basic situation of each county in Environment Support System. The results of the ranking are listed in Table 4: Table 4 Ranking of each county in Environment Support System Prefecture/County

Z31

Ranking 7

Z32

Ranking

Z3

Ranking

Hotan Prefecture Moyu County

0.65

14.75 15

0.64

17.75 16

0.65

17.13 16

Pishan County Yutian County

0.66 0.73

13 8

0.12 0.4

29 24

0.44 0.59

26 22

Lop County Qira County

0.18 0.8

28 2

1.56 0.38

5 26

0.75 0.63

13 18

0.65 0.3

16 24

1.17 0.42

13 23

0.86 0.35

10 27

0.68

12

1.51

6

1.02

5

0.22 0.58

18.44 26 18

0.42 3.85

10.22 22 1

0.3 1.93

11.22 29 1

Shufu County Yecheng County

0.23 0.84

25 1

2.33 1.39

2 7

1.1 1.07

2 3

Jiashi County Yopurga County

0.45 0.58

21 19

0.47 1.16

21 14

0.46 0.82

25 11

Shache County Shule County

0.76 0.52

6 20

1.38 1.24

9 12

1.02 0.82

4 12

Bachu County Kirgiz autonomous prefecture of Kizilsu Akto County

0.06

30

1.73

4

0.75

14

0.78

4

0.47

20

0.65

17

Wuqia County Akqi County

0.41 0.73

22 9

0.16 0.17

28 27

0.31 0.5

28 24

Artux City

0.63

17

0.51

19

0.58

23

0.78 0.68

8 5 11

1.28 0.64

13.5 10 17

0.99 0.66

11 7 15

0.66

14 14

1.26

11 11

0.91

9 9

0.17

26 29

0.11

22.5 30

0.15

25.5 30

0.37

23

0.95

15

0.61

21

0.74 0.8

5 7 3

1.39 0.4

15.5 8 25

1.01 0.63

12.5 6 19

0.19

18.5 27

1.96

10.5 3

0.92

14 8

0.69

10

0.53

18

0.62

20

Hotan County Minfeng County Hotan City Kashi Prefecture Taxkorgan County Yengisar County

Aksu Prefecture Kalpin County Wushi County Hami Prefecture Barkol County Tacheng Prefecture Hoboksar County Toli County Altay Prefecture Qinghe County Jeminay County Ili Prefecture Qapqal County Nilk County

13

23.5

23

3.4 Social Support System（Z4） The relevant variable produces two principal components: F41 and F42 . F41=0.539Z41-0.193Z42+0.679Z43 8

F42=0.037Z41+1.026Z42-0.229Z43 The integrated function of Social Support System is: Z4=（0.577/0.83）F41+（0.253/0.83）F42 The results of this ranking are listed in Table 5. Table 5

Ranking of each county in Social Support System

Prefecture/County

0.7596

Ranking 17.88 19

0.33641

Ranking 17.88 21

0.63

Ranking 20.63 25

Pishan County Yutian County

1.04828 0.57285

12 28

0.41077 0.6186

20 14

0.85 0.59

18 27

Lop County Qira County

0.6913 0.97276

22 13

1.24138 0.16547

5 27

0.86 0.73

15 23

0.51575 0.89512

30 15

0.43864 0.30546

18 23

0.49 0.72

30 24

1.42344

4

0.52902

15

1.15

3

0.67539 0.67238

22 23 24

0.43645 0.94932

15.11 19 9

0.6 0.76

19.33 26 20

Shufu County Yecheng County

0.62683 0.64379

27 26

0.16228 1.05381

28 7

0.49 0.77

29 19

Jiashi County Yopurga County

1.45087 0.70577

3 21

0.18186 0.82541

26 13

1.06 0.74

6 22

Shache County Shule County

0.52028 0.6599

29 25

2.24684 1.31134

1 4

1.05 0.86

7 17

Bachu County KirgizAutonomous prefecture of Kizilsu Akto County

0.73846

20

0.10018

29

0.54

28

0.8696

10.25 16

0.49158

14.5 16

0.75

11.25 21

Wuqia County Artux City

1.11707 1.17897

11 6

0.8274 0.98794

12 8

1.03 1.12

8 4

Akqi County

1.1576

8

0.32813

22

0.9

12

0.86191 1.52954

9.5 17 2

0.86541 0.04877

20.5 11 30

0.86 1.08

10.5 16 5

1.14386

10 10

0.47291

17 17

0.94

10 10

0.89788

11.5 14

0.92007

6.5 10

0.9

6.5 11

1.14397

9

1.40933

3

1.22

2

Hotan Prefecture Moyu County

Hotan County Minfeng County Hotan City Kashi Prefecture Taxkorgan County Yengisar County

Aksu Prefecture Kalpin County Wushi County Hami Prefecture Barkol County Tacheng Prefecture Toli County Hoboksar County Altay Prefecture

F1

6

F2

24.5 9

Z

11.5

Qinghe County Jeminay County Ili Prefecture Nilk County Qapqal County

1.31845 1.16909

5 7

0.20177 0.1902

24 25

0.98 0.87

9 14

2.36474

9.5 1

1.44684

4 2

2.08

7 1

0.77735

18

1.12579

6

0.88

13

3.5 Knowledge Support System The meaning of the title “knowledge support system” can be derived by looking at its components, namely, levels of education, science and technology, and management. The relevant variable gives birth to three principal components: F51, F52 and F53. F51=1.001Z51+0.014Z52+0.017Z53 F52=0.014Z51+1.002Z52-0.032Z53 F53=0.0107Z51-0.032Z52+1.002Z53 The integrated function of Knowledge Support System is: Z5=0.363F51+0.325F53+0.312F53 The first principal component F51 can show us the power of education in each county. The second principal component, F52, express the power of science and technology in each county. The third principal component, F53, represent the power of management. The results and rankings are listed in Table 6: Table 6

Ranking of each county in Knowledge Supporting System

Prefecture/County

F51

Hotan Prefecture Moyu County

Ranking

F52

17

Ranking

F52

18.13

Ranking

Z5

21.75

Ranking 18.38

Pishan County

0.6996 0.8236

29 19

0.3236 0.8217

30 19

0.8323 0.6484

14 23

0.62 0.77

30 23

Yutian County Lop County

0.815 0.8257

20 18

0.9218 0.972

15 12

0.645 0.6436

24 25

0.8 0.82

19 18

Qira County Hotan County

0.923 0.9035

12 14

0.9068 0.9466

16 13

0.5373 0.5357

29 30

0.8 0.8

20 21

Minfeng County Hotan City

1.1865 0.8139

3 21

1.0071 0.3379

11 29

0.6487 1.064

22 7

0.96 0.74

12 25

0.8583

21.78 17

1.1955

16.67 7

1.1773

16.44 5

1.07

19.11 9

Yengisar County Shufu County

0.8023 0.7418

22 25

0.7623 0.6286

22 26

0.6199 0.7032

27 20

0.73 0.69

26 29

Yecheng County Jiashi County

0.7138 0.7783

27 23

1.2533 0.9285

5 14

0.8727 1.975

13 1

0.94 1.2

14 6

Yopurga County Shache County

0.7646 0.7132

24 28

0.7469 0.7695

23 21

0.7698 0.6681

18 21

0.76 0.72

24 27

Kashi Prefecture Taxkorgan County

10

Shule County Bachu County Kirgiz autonomous prefecture of Kizilsu Akto County Wuqia County

1.3612 0.3608

4 28

1.5966 0.9732

10.5 2 7

1.6755 0.9272

1 11

Aksu Prefecture Kalpin County Wushi County Hami Prefecture Barkol County

Akqi County Artux City

Tacheng Prefecture Hoboksar County Toli County Altay Prefecture Qinghe County Jeminay County Ili Prefecture Nilk County Qapqal County

0.7413 1.1399

26 4

0.6396 0.8088

26 17

0.91 0.78

15 22

0.6535 1.1247

20.5 25 8

0.9374 0.9414

19.5 10 9

1.08 1.01

11.5 8 10

1.2241 2.1669

6 2

0.9705 0.8771

8 12

1.31 1.31

3 2

0.9157

9 13

0.665

20.5 24

0.8952

13.5 11

0.83

15 17

1.128

5

0.8903

17

0.8316

16

0.96

13

0.9458

10 10

0.871

18 18

0.729

19 19

0.85

16 16

1.1145 0.9538

7 6 8

1.0851 0.7942

15 10 20

1.6156 1.2519

3.5 3 4

1.26 0.99

7.5 4 11

0.8711

12 15

0.4262

15 27

0.832

10.5 15

0.71

16.5 28

0.9531

9

1.5685

3

1.1369

6

1.21

5

0.6368 0.8707

23 30 16

1.1039 2.7352

5 9 1

1.7468 0.6076

15 2 28

1.13 1.4

4 7 1

4

Conclusions

Based on the results of analysis above, some recommendations can be drawn about the most effective way to address poverty alleviation in the survey regions. That is, it identifies areas (discussed as indicators above) that should be targeted for future investment for the regions, prefectures and counties that were included in the survey. The areas (indicators) for investment that should receive the highest priority are listed first.

Table 7. Recommended direction of investment by geographical regions Region South of Xinjiang

East of Xinjiang

A. B. C. D. E. A. B.

Direction of Investment Enhancing the investment to agriculture Enhancing the efficiency of resource Developing the tertiary industry Enhancing the educational level Improving the situation of ecosystem Enhancing the infrastructural investment Enhancing the educational level 11

North of Xinjiang

Table 8

Recommended direction of investment by prefecture

Prefecture Hotan

A. Enhancing the infrastructural investment B. Enhancing the educational level C. Improving the situation of ecosystem

Direction of Investment A. Improving the level of urbanization B. Developing rural education C. Improving the quality of cultivated land D. Improving the power of ecosystem resisting counter-reaction

A. Improving the level of regional education B. Improving the power of regional science and technology C. Improving the industrial structure Kirgiz Autonomous A. Improving the condition of agricultural Prefecture of Kizilsu production B. Improving the power of ecosystem resisting Kashi

counter-reaction Aksu

Hami Tacheng

A. Enhancing the investment of agriculture B. Improving the condition of agricultural production A. Improving the infrastructure B. Developing the non-agricultural industry A. Improving the power of ecosystem resisting counter-reaction

Altay

Ili

Table 8

B. A. B. C.

Improving the infrastructure Improving the infrastructure Developing the non-agricultural industry Improving the power of regional science and technology A. Improving the efficiency of resource using B. Improving the infrastructure

Recommended direction of investment by county

County

Direction of Investment Improving the quality and quantity of per capita resource

Hotan city, Minfeng county, Qira county, Kalping county, Akto county, Pishan county, Qinghe county, Toli county, Lop county, Taxkorgan county, Qapqal County, Wushi County Jeminay County, Qinghe county, Shufu County, Lop county, Akqi county, Barkol County, Toli county, Shache County, Hotan county, Qira county Hoboksar County, Taxkorgan county, Wuqia

Enhancing infrastructural investment Enhancing soil improvement 12

County, Minfeng county, Pishan county, Jiashi County, Akqi county, Artux City, Yutian County, Toli county, Nilk County, Wushi County Hotan county, Shufu county, Bachu County, Yutian county, Taxkorgan county, Moyu County, Minfeng county, Qira county, Yopurga County, Akto County, Yecheng County, Shule County Moyu county, Shufu county, Qinghe county, Shache county, Yengisar County, Hetian city, Yopurga County, Pishan county, Bachu County, Hetian county

13

and rising the productivity of cultivated land Improving the quality of labour and developing the tertiary industry Developing the rural education and popularizing the suitable agricultural technology