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The impact of the great western development strategy on three provinces of Northwestern China.


Over the past three decades, the People's Republic of China has experienced extraordinary and transformative economic growth. However, this prosperity and increased welfare has not been evenly distributed throughout the country. A 1999 evaluation of the Human Development Index (HDI) in China revealed that all eastern provinces in China had a high level of .711 to .853, central provinces had a ranking of .673 to .732, and western provinces had the lowest ranking of .521 to .751 on the HDI scale (Hu 2007). In part, this is due to the post-Maoist development strategy which focused preferential policies on the eastern coastal region (Fan 1997). The eastern provinces were given priority due to perceived geographical comparative advantages, notably their proximity to ocean trade routes.

In 1999 Chinese policymakers signaled a significant departure from prior regional development policies by announcing Xibu Da Kaifa, also known as the Great Western Development Strategy (GWDS). The strategy was designed to confront economic and ecological concerns as well as issues of human capital in the western provinces. Specifically, policymakers sought to cultivate a good investment environment, develop a strong labor force, and promote conservation policies. The Great Western Development Strategy aimed to resolve regional disparities through investment in infrastructure, conservation programs, and health and social institutions. Encompassing over seventeen provinces and totaling over a trillion yuan in expenditures, this strategy reflects both depth of application and breadth of focus (Goodman 2004).

The correlation between infrastructure and development has been well-established in previous economic literature, and has played a key role in the rationale behind the GWDS (Zou, Zhang, Zhuang, and Song 2008). However, the impacts of infrastructure on the environment and specifically on northwestern China's provinces are not fully understood (Fleisher, Li, and Zhao 2010). Therefore, the purpose of this study was to examine the effectiveness of the GWDS investment in infrastructure and its ramifications for the environment, social well-being, and the economy of the northwest. Our research encompassed qualitative as well as quantitative analysis in order to develop a more comprehensive understanding of infrastructure's role in promoting sustainable development.

The following section will present background information on the northwest provinces as well as a brief review of the literature that relates to our topic. The second segment of this paper will explain our hypotheses and quantitative and qualitative methodologies. Finally, we will reveal our findings, present our interpretations of the results, and draw conclusions.


Our research focused on comparative development in three provinces of China's northwest in relation to conditions throughout the country. This section will provide an important context for understanding China's three northwestern provinces: Gansu, Shaanxi, and Ningxia Autonomous Region.

Gansu is one of China's slowest growing provinces, but paradoxically it has received one of the largest amounts of investment from the GWDS (Wei et al. 2006). The main reason behind Gansu's low growth rate is the economic domination of State-Owned Enterprises (SOE's), which represent Gansu's largest economic sector and which have a very low growth rate of 2.94 percent (Wei et al. 2006). In addition, Gansu's isolated location in China's interior limits potential for foreign investment and is the reason why Gansu has the fifth lowest amount of Foreign Direct Investment (Wei et al. 2006). Gansu is also troubled by a lack of human capital, a shortage of high-skilled jobs for educated individuals, and high levels of inequality (Wei et al. 2006).

Like Gansu and other western provinces, Shaanxi was left behind in the 1970's, when China's growth first accelerated (Vermeer 2004). However, Shaanxi is rich in natural resources such as coal and natural gas, and this comparative advantage allowed the energy industry in Shaanxi to become one of the province's main instigators of growth. In addition to extractive industries, Shaanxi is quickly developing tertiary tourism and information technology industries. Government investment in infrastructure, subsidies, irrigation, and development of high-value-added crops has helped improve agriculture in Shaanxi, but inequalities continue to exist within the province between rural and urban areas (Vermeer 2004).

Unlike the other northwestern provinces, the Ningxia Autonomous Region contains a large minority population, the Hui Muslims. Historically, the Hui have been both economically and socially disadvantaged in comparison to the majority Han population; currently, the majority of the Hui people live in the poorest areas of the autonomous region and generally have low literacy rates, especially among the female population (Ho 2003). Ningxia faces many environmental challenges including soil erosion, desertification, and salinization. As a result, arable land is of poor quality and agricultural returns are low (Merkle 2003). Even with its salient environmental concerns, Ningxia has seen a dramatic rise in the standard of living since the establishment of GWDS. Rural income more than doubled between 1999 and 2007. In addition, Ningxia experienced increases in employment and output growth in manufacturing and service industries. Even though Ningxia is moving away from primary industry, a majority of the population continues to be employed in that sector.

Trends across time for several critical variables can be illustrated by using location quotients to compare these northwestern provinces with national statistics before and after GWDS. A location quotient is a measure of inequality between a region and the nation as a whole that designates the national variable as 100. If the location quotient is above 100, then the region is performing better than the national level; if it is lower than 100, the region is performing worse than the national level; and if it is equal to 100, then the region is at the same level as the nation. As an overview of relationships that are later tested for statistical significance, location quotients were calculated for 1999, 2002, 2005 and 2008 to portray trends across several illustrative variables that have occurred during the GWDS period. (2)

Figure 1 shows that beginning in 1999, employment in primary industries (Agriculture, Forestry, and Mining) in the aggregation of our three northwestern provinces was significantly higher than national averages.


Between 1999 and 2002 there was a significant decline in employment in primary industries; as employment in primary industries declined there was a rapid rise in secondary industry (manufacturing) employment, which began well below national levels in 1999. While the northwestern employment rate for secondary industries remains slightly below national levels, it is evident that there has been a structural shift from primary to secondary industries in the three province aggregation. Although the structural shift lagged that of the nation in the earlier period, the three provinces have, in aggregate, begun to track the nation in the transition towards secondary industries.

Figure 2 illustrates that Gansu has experienced the least structural change among the northwest provinces, as its primary output continues to increase relative to national averages.


The location quotient on road density is represented in Figure 3.


While road density in Shaanxi and Gansu has increased substantially in relation to national levels, Ningxia has experienced a lower level of change. Road density in Shaanxi Province has consistently remained above the national mean because of its role as the transportation hub to the west.

Figure 4 illustrates that in 1999 student enrollment in Ningxia's and Gansu's secondary schools was below national averages, but has improved measurably.


Infrastructure Literature

David Allen Ashauer investigated the association between infrastructure and growth by examining the relationship between productivity and stock and flow government spending variables. He found that core infrastructure is the largest contributing factor to productivity and accordingly leads economic growth (Ashauer 1989). More recent research observed a strong positive correlation between economic growth and infrastructure development (Barrios 2008, Fan et al. 2004, He et al. 2009). Their research concluded that "rural roads generate the largest impact in terms of the rural development index and income growth" (2008). Other studies supported this finding as it applies to China specifically and found that disparities in infrastructure play a significant role in the manifestation of regional inequalities and that infrastructure development would help reduce those inequalities (Fan et al. 2004, He et al. 2009). In addition, public investment in transport infrastructure, specifically, road construction in poor areas, has been found to lead to increases in growth and poverty alleviation (Zou et al. 2008). Furthermore, the research suggests that infrastructure development may have significant impacts on other areas of human society, namely human capital (Barrios 2008, Bryceson et al 2008). Their previous studies concluded that infrastructure can also increase the accessibility of health, education, and social services (Barrios 2008, Bryceson et al. 2008).

However, there are also numerous studies that have revealed the deficiencies of infrastructure in promoting growth. Belton Fleisher's research on human capital in China indicated that investments in education served to reduce regional inequalities much more effectively than telecommunications and road infrastructure (2010). Telecommunications infrastructure was found to have a higher rate of return in developed--not developing--regions, and roads did not have a positive or significant impact on total factor productivity growth (Fleisher et al. 2010). In addition, several investigations into the correlation between infrastructure investment and private productivity revealed that there was little correlation and no significant evidence of causation (Garcia-Mila et al. 1996; Holtz-Eaken 1994). As for China-specific studies, the relationship between infrastructure and development is not treated in the available literature to any degree of statistical significance.


The objective of the study was to assess the various impacts of infrastructure improvement in China's northwestern provinces. In our study, infrastructure is defined as roads, electricity, and water infrastructure. Water infrastructure includes methods of both transporting and storing water. Specifically, we analyzed the economic, social, and environmental ramifications of the policy in the three provinces of Shaanxi, Gansu, and Ningxia, using both quantitative data and qualitative field observations. A regional comparison analysis using cross-sectional data was conducted to test the role of GWDS infrastructure investment.

In this study, economic, social and environmental changes were assessed using certain key variables. These included: income per capita, industry shifts from primary to secondary, exports, crop shifts from low-value-added crops to high-value-added crops, secondary education, and afforestation. The intent was to understand the magnitude of the relationship that exists between income levels and each of the above listed variables. Possible hypotheses about the role of infrastructure in the three provinces were developed and tested.

Quantitative Hypotheses

Table 1 summarizes the hypotheses that were addressed in the quantitative testing.

We hypothesized that there would be a negative relationship between our 1999 control variable and the majority of our dependent variables. We based this upon the concept that the lower the initial starting point, the greater the potential for increases. For example, the lower the initial income, the higher the potential for income to increase. Therefore there should be a negative relationship between income and the control variable. The exceptions to this negative relationship would be secondary education, primary industry, and agricultural loans, because there must be a certain threshold level of these variables in order for increases to occur. In order for agricultural loans to increase, there must be a certain level of credit, which is established by agricultural loans. Therefore the relationship would be positive for agricultural loans. In contrast, we hypothesized that the relationships between GWDS and the majority of our dependent variables would be positive. This is because we believe our dependent variables are indicators of growth, which would be augmented by the GWDS. Primary industry and agricultural loans were an exception, because we predicted an overall decrease in primary industry and agriculture as structural change occurs and industries shift from primary to secondary and tertiary industries. The variable for irrigated areas was also an exception due to the Grain for Green afforestation program; the predicted shift from high water-intensive to low water-intensive crops; and the shift from agricultural production as a primary industry to secondary and tertiary industries which do not irrigate land.


Quantitative Methodology

To test our hypotheses quantitatively, we developed a formal econometric model and used provincial data from the China Statistical Yearbooks. GWDS was the identifier to distinguish between provinces affected by the strategy and those that were not. The primary investment in GWDS is infrastructure. We analyzed the relationship between GWDS, our independent variable, and the several dependent variables that were listed previously. An ordinary least squares regression model was used to test for statistical validity and significance. Additionally, regional comparative analysis was conducted by comparing Shaanxi, Gansu, and Ningxia Autonomous Region with the rest of the western, central and coastal provinces.

Equations and variables are shown in Table 2, as follows:

These equations were formulated in such a way as to minimize multicollinearity. We selected 1999 as a base year, because it preceded the implementation of GWDS. This allowed us to control for the short-term high growth rates that occur in the initial stages of development. We also used population density as our control variable to take into account the impact that population can have on development indicators since some provinces may indicate higher levels of growth due more to population growth than to the variables we tested.

Aggregate dependent variables were parameterized in order to take into account differences between provinces. This was achieved by dividing the dependent variable by the total population, land area, or, in the case of aggregate loans, by total loans. The parameterizations are:

* Secondary Education / Total Population (%)

* Primary Industry / Total # Employed

* Secondary Industry / Total # Employed

* Irrigated Areas / Total Area

* Secondary Education / Total Population

* Agricultural Loans / Total Loans

* Population/ Land Area

* The design of the model is intended to highlight changes that have resulted due to the implementation of GWDS.

Qualitative Methodology

In addition to our quantitative analysis, we also conducted observational research in western China. We began by attending the 7th Annual Conference of the Consortium for Western China Development Studies in Chengdu, the capital of Sichuan province. After the conference, we began the field research portion of our analysis, which consisted of a three weeks of travel through a variety of villages in Shaanxi, Gansu and Ningxia provinces. In these villages we conducted interviews with business leaders, government officials, farmers, and residents. Our interviews were designed to assess the intensity and diversity of impacts of infrastructure and the Great Western Development Strategy on their community.

Qualitative Hypotheses

Table 3 summarizes the hypotheses addressed by our qualitative research.

Data Sources:

For our quantitative analysis, we relied on the China Data Online database of the University of Michigan, using the annual macro-economic statistics at the provincial level. We extracted data for every Chinese province and autonomous region excluding Beijing, Tibet, Shanghai, Tianjin, Chongqing, and Hainan. The data encompassed the years 1999, 2003, and 2007, and covered the wide range of economic, social, and environmental variables that were appropriate to the hypotheses.


Quantitative Findings:

Table 4 illustrates the statistical findings of the regression analysis. Overall, these findings indicate that the GWDS had a positive impact on development. However, the findings are partially clouded by a lack of statistical significance to many of the hypotheses. This was due mainly to the limited number of data points, the inaccuracy of the data, and the time lags associated with implementation of GWDS investments and policies. The quantitative findings confirmed our predictions that the relationship between income per capita and the GWDS was positive, as was the correlation between length of highways and the GWDS. We also found that secondary education and the GWDS share a positive correlation, as we had hypothesized. The data also indicated the predicted structural changes away from primary industries, as agricultural loans, primary industry, and irrigated land share a negative relationship with the GWDS.

While many of our hypotheses were confirmed by the regression analysis, the results did contradict several of our predictions. Our findings revealed three unexpected relationships between the GWDS and the dependent variables. The hypotheses that rural income, secondary industry, and fixed asset investments would share a positive relationship with the GWDS were invalidated. The development emphasis on urban, rather than rural, growth explains the negative correlation between GWDS and rural income. The negative relationship between secondary industry and the GWDS may be explained by the time-consuming nature of policy implementation and the construction of infrastructure, which is important for developing secondary industries. Fixed-asset investments and the GWDS may share a negative relationship as a result of time lags in policy implementation and in the development of fixed-asset investments. With only a few exceptions, our quantitative analysis demonstrated that the GWDS has had a positive impact overall on these various indicators of development; however, the strength of this correlation was limited by the lack of data points, the delay in policy implementation, and the lack of completely reliable data.

Comparative Regional Statistics

Table 5 illustrates several noteworthy statistical trends. It is clear from these data that the western region has experienced rapid economic, social, and environmental changes during the period of implementation of the GWDS. In particular, GDP per capita and secondary education experienced the greatest rate of change in the west compared with the rest of the country. However, great disparities exist even within the western region, and more specifically within the northwest.

While Shaanxi province and Ningxia Autonomous Region experienced significant economic growth, Gansu continued to lag behind most other western provinces in terms of many indicators of development. As for structural change, Gansu's rate of change from primary to secondary industries was the lowest by far among the northwestern provinces, the western region, and China as a whole. In almost ten years, Gansu's rate of change towards secondary industries was only about 1 percent, and its rate of change away from primary industries was only about 7.6 percent, both well below all other regions. In addition, while Shaanxi's GDP per capita percentage change was the highest in the west and was above national averages, Gansu experienced a much slower rate of change, below that of other regions and the country as a whole. For the 1999 base year, Gansu was the least-developed province in the northwest; therefore, we had expected to see the greatest amount of change in Gansu.

Qualitative Findings:

Between July 8th and July 21st, our team traveled through rural villages in the provinces of Shaanxi, Gansu, and the Ningxia autonomous region to collect observations pertaining to the impact of the GWDS in these areas. We observed numerous trends, some reflecting the successes of the policy and others revealing areas in need of improvement.

Economic Trends

While developing our proposal for this research project, we hypothesized that there would be a shift from low-value-added crops to high-value-added crops as a result of GWDS policies. During interviews conducted with rural farmers and industry leaders, we found that there has been heavy government encouragement in the form of technology transfers, subsidies, and special technical training courses for farmers to transition from subsistence to cash crop farming. This transition correlated with a significant increase in income for many farmers and industries. In addition, government support was tailored to a region's specific geographical characteristics, such as support for developing arid-resistant crops in drier areas of the northwest.

Microcredit is a form of infrastructure that we had not considered in our proposal, and one that has proven to be a key part of development in many rural areas. Microcredit associations, which may be private or public entities, provide rural residents with convenient and expedient access to small-scale loans. Many rural residents do not have the credit to apply and receive loans from most state banks, so these microcredit institutions are often the only means for residents to acquire loans. We found that microcredit associations throughout the northwest had very different experiences and success rates; overall, microcredit was revealed to be an important aspect of development.

Social Trends

In addition to investment in infrastructure, the GWDS has placed emphasis on developing educational institutions and resources. Every village that we visited in the northwest provinces had both a pre-school and elementary school, whereas middle schools were located in the county seats and high schools in larger cities. Due to improvements in transportation infrastructure, even schools located outside of villages were now accessible to rural students. However, few universities and colleges are located in the west, and virtually none are in proximity to rural areas. Therefore, while primary school attendance is very high, only 20% of students continued onto high school and only 1% of students enrolled in a university or college. Consequently, education remains an area in need of greater investment and reform.

Those students who did continue on to college rarely returned to their hometowns; rather, they became part of a growing migrant population streaming out of rural areas. In many villages, migrant labor comprised an important part of household and village income. Wages from migrant labor represented a form of diversification of income and was beneficial for many families; however it resulted in significant demographic issues. Permanent village populations were composed mostly of elderly parents and their grandchildren; parents, spouses, and older children separated from their families to live and work in cities.

For some villages, migration has increased due to the construction of roads which facilitate travel to bigger cities. While many western provinces benefited from large labor populations, lack of available jobs motivated potential employees to separate from their families and work elsewhere. However, in other regions, migration has decreased due to the creation of new local industries which can provide employment opportunities

Environmental Trends

One of the most pressing challenges facing northwest provinces is the issue of water quantity, which threatens agricultural and industrial production as well as access to drinking water. When asked how they expect to confront issues of water quantity, rural residents placed their faith in the government to develop a solution. While there is significant government investment in water infrastructure such as reservoirs and wells, there is also a lack of policies that could benefit water conservation.

In the northwestern provinces, there is currently no established system of water rights or water management. Urban residents rely mostly on tap water and pay little if anything for consumption. The development of a system to allocate water more effectively, and hopefully conserve it, would promote sustainability but may also lead to restrictions on growth. Over the next 10 years we expect to see the development of water rights systems to address the current shortage of means to allocate effectively and conserve limited water resources.

Political Trends

It should be noted that in the course of our observational research, we found that villages throughout the counties we visited had a wide range of experiences with the GWDS. In particular, we noticed great dissimilarities between majority ethnic Han villages and those villages dominated by the Hui Muslim minorities in Ningxia. In several Hui villages, residents reported to us that they were receiving little, if any, government support. While the same policies were supposed to be implemented across Han and Hui villages, some Hui villages were receiving much more limited government infrastructure investment, subsidies, and other forms of support.

In summary, our observations and interviews indicated improvements in the standard of living and overall economic growth. However, we also became aware of the lack of local employment opportunities, disparities within provinces, lack of human capital, water resource concerns, and lack of industries.

Conclusion and Policy Recommendations

In researching the impacts of the GWDS in Gansu, Shaanxi, and Ningxia, we observed numerous economic, social, and environmental trends. Overall we found that infrastructure development has had a positive impact on the rural villages we visited. Roads led to more regional and international trade, opened up access to and created new markets, and as a result led to an increase in incomes and production. Electrical, water, and telecommunications infrastructure helped to improve dramatically residents' standard of living. All types of infrastructure development created better environments for enterprises and industries. However, in recognizing the many positive impacts of the GWDS, we also became aware of several issues that should be addressed by Chinese policymakers in the second phase of this strategy's implementation.

Western China in general suffers from a lack of human capital investment and a shortage of higher education resources. This deficiency impacts employment opportunities, the willingness and ability of high-tech industries to move into the west, and migration patterns. The irony of migration is that while it adds to individual village and household incomes, it significantly limits the potential of a village to develop, because of the loss of educated and skilled labor. In addition to having a greatly untapped labor population, the west enjoys rich natural resources that could be utilized more efficiently to stimulate growth. As opposed to extracting and exporting these resources to be processed elsewhere, there is great potential for secondary industries to prosper in the west and make use of this abundance of natural resources and better utilize the large low-wage labor population. While the west enjoys the comparative advantages of natural resources, it also suffers from the geographical disadvantages of being a mostly arid region. Therefore, future development must take into consideration how water resource issues will be managed and overcome. Given the fragile nature of the northwestern ecology, sustainability and the quality of growth must become priorities in the next phase of the west's regional development.

As illustrated by our descriptive statistics, the western region has experienced great change, especially in terms of economic growth. However, the data also reveal sizeable disparities even within the west and more specifically among the three northwestern provinces. While Shaanxi and Ningxia are leading western provinces, Gansu continues to lag behind in terms of economic development and social qualities. Furthermore, our statistical analysis demonstrated that growth has been unequal within provinces, specifically between urban and rural areas. Our quantitative findings illustrated that while growth has been achieved in many indicators of development, rates of change have not always been significant. This is likely due to the fact that the GWDS promotes policies and changes that have longterm impacts, rather than quickly-realized effects. Therefore, we expect to see more significant levels of growth in the next ten years of the GWDS.

The Great Western Development Strategy will face numerous challenges in the next ten years of the program. However, there have also been many noteworthy successes. Living standards have increased markedly since the 1990's for millions of villagers. Entrepreneurship is now a possibility for even poor rural families. The Great Western Development Strategy is a massive program, totaling trillions of RMB in investments, affecting millions of people, and covering thousands of miles. As a result, there is still much to be learned about the policy's diversity of impacts on rural societies. We believe that our findings have helped expand the understanding of China's ever-evolving regional development strategy and have highlighted the significant role the GWDS is playing in encouraging China's overall growth.


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Isa Harrison, Central Washington University

Meredith Houck, University of North Carolina-Ashville

Naushin Jiwani, New College of Florida

Richard Mack, Central Washington University

Jennie Welch, Bucknell University

(1) We thank the National Science Foundation for making our research possible through its Research Experience for Undergraduates Program.

(2) All national figures were summed from the data of each individual province, excluding Tibet, Beijing, Shanghai, Tianjin, Chongqing and Hainan. Aggregate location quotients were calculated treating a sum of the three provinces' values of the variable as a single region to be compared to the national level.
Table 1: Quantitative Hypotheses

                      Independent Variables
Dependent Variables   1999 (base year con-    GWDS
                      trol variable)

Income per Capita     Negative relationship   Positive relationship
Rural Income per      Negative relationship   Positive relationship
Length of Highways    Negative relationship   Positive relationship
Secondary Education   Positive relationship   Positive relationship
Agricultural Loans    Positive relationship   Negative relationship
Primary Industry      Positive relationship   Negative relationship
Secondary Industry    Negative relationship   Positive relationship
Irrigated Areas       Negative relationship   Negative relationship
Fixed Asset           Negative relationship   Positive relationship

Table 2: Regression Analysis Equations

Equation   Equation

1          %[DELTA]Y = a - [b.sub.1] [Y.sub.1999]
           + [b.sub.2] POP + [b.sub.3] GWDS
2          %[DELTA]RY = a - [b.sub.1] [RY.sub.1999]
           + [b.sub.2] POP + [b.sub.3] GWDS
3          %[DELTA]LH = a - [b.sub.1] [LH.sub.1999]
           + [b.sub.2] POP + [b.sub.3] GWDS
4          %[DELTA]SE = a - [b.sub.1] [SE.sub.1999]
           + [b.sub.2] POP + [b.sub.3] GWDS
5          %[DELTA]AGL = a - [b.sub.1] [AGL.sub.1999]
           + [b.sub.2] POP + [b.sub.3] GWDS
6          %[DELTA]PI = a - [b.sub.1] [PI.sub.1999]
           + [b.sub.2] POP + [b.sub.3] GWDS
7          %[DELTA]SI = a - [b.sub.1] [SI.sub.1999]
           + [b.sub.2] POP + [b.sub.3] GWDS
8          %[DELTA]IA = a - [b.sub.1] IA1999
           + [b.sub.2] POP + [b.sub.3] GWDS
9          %[DELTA]FAI = a - [b.sub.1] [FAI.sub.1999]
           + [b.sub.2] POP + [b.sub.3] GWDS

POP =      Population Density
GWDS =     Implementation of Great
             Western Development Strategy
Y =        Income Per Capita
RY =       Rural Income Per Capita
LH =       Length of Highways
SE =       Secondary Education
AGL =      Agricultural Loans
PI =       Primary Industry
SI =       Secondary Industry
IA =       Irrigated Area
FAI =      Fixed Asset Investment

Table 3: Qualitative Hypotheses

Income            Affects   Exports;     Industry Shifts;
                            Positively   Positively

Microcredit       Affects   Income;      Exports;
                            Positively   Positively
Education         Affects   Income;      Exports;
                            Positively   Positively
Road              Affects   Income;      Exports;
Infrastructure              Positively   Positively
Industry Shifts   Affects   Income;      Exports;

                            Positively   Positively
Electrical        Affects   Income;      Exports;
Infrastructure              Positively   Positively
Water             Affects   Income;      Exports;
Infrastructure              Positively   Positively

Income            Crop Shifts;       Education;
                  Positively         Positively

Microcredit       Industry Shifts;   Crop Shifts;
                  Positively         Positively
Education         Industry Shifts;   Crop Shifts;
                  Positively         Positively
Road              Industry Shifts;   Crop Shifts;
Infrastructure    Positively         Positively
Industry Shifts   Education;         Water
                  Positively         Positively
Electrical        Industry Shifts;   Crop Shifts;
Infrastructure    Positively         Positively
Water             Industry Shifts;   Crop Shifts;
Infrastructure    Negatively         Positively

Income            Afforestation;   Water
                  Negatively       Infrastructure;

Education         Migration;
Road              Education;       Health Services;
Infrastructure    Positively       Positively
Industry Shifts   Migration; Positively
                  Road Infrastructure;
Electrical        Water Infrastructure;
Infrastructure    Positively

Income            Migration; Minimum
                  Income Required



Road              Migration;
Infrastructure    Positively
Industry Shifts


Table 4: Empirical Findings

Model               Independent Variable         [R.      Adjusted
                                                 sup.2]   [R.sup.2]

1) Income                                        0.053    -0.083
Per Capita          Income / Capita--1999
                    GWDS Presence
2) Rural Income/                                 0.208    0.095
Household           Rural Income /
                    GWDS Presence
3) Highway Length                                0.198    0.084
                    Highway Length--1999
                    GWDS Presence
4) Secondary                                     0.223    0.152
Education           Population--1999*
                    GWDS Presence**
5) Agricultural                                  0.102    -0.026
Loans               Agricultural Loans--1999
                    GWDS Presence
6) Primary                                       0.477    0.404
Industry            Primary Industry--1999
                    GWDS Presence
7) Secondary                                     0.431    0.349
Industry            Secondary Industry--1999**
                    GWDS Presence
8) Irrigated                                     0.267    0.162
Area                Irrigated Area--1999
                    GWDS Presence
9) Fixed Asset                                   0.155    0.034
Investment          F.A. Investment--1999*
                    GWDS Presence

Model               Standard    Coefficient   T-Stat   P-Value

1) Income           55.682
Per Capita          0.005       -0.002        -0.294   0.771
                    0.082       0.037         0.448    0.659
                    33.071      26.439        0.799    0.433
2) Rural Income/    21.385
Household           0.008       -0.017        -2.173   0.041

                    0.032       0.042         1.332    0.197
                    13.358      -3.281        -0.246   0.808
3) Highway Length   77.499
                    31727.427   -39397.225    -1.242   0.228
                    0.131       0.093         0.709    0.486
                    43.415      26.223        0.604    0.552
4) Secondary        23.105
Education           0.0328      0.057         1.755    0.093
                    12.791      32.141        2.513    0.0198
5) Agricultural     73.269
Loans               12626.138   10752.325     0.852    0.404
                    0.109       -0.104        -0.948   0.354
                    41.622      -48.482       -1.165   0.257
6) Primary          9.804
Industry            0.273       0.115         0.423    0.677
                    0.014       -0.056        -3.989   0.001
                    6.453       -8.867        -1.374   0.183
7) Secondary        22.287
Industry            1           -2.842        -2.841   0.0098
                    0.032       0.103         3.204    0.004
                    14.038      -2.751        -0.196   0.847
8) Irrigated        11.207
Area                0.002       0.002         1.046    0.308
                    0.018       -0.049        -2.734   0.012
                    6.205       -9.097        -1.466   0.157
9) Fixed Asset      194.37
Investment          0.089       -0.171        -1.939   0.066
                    0.384       0.439         1.144    0.265
                    107.655     -24.068       -0.224   0.825

* = Significant at the 90th percentile, ** = Significant at the 95th
percentile, *** = Significant at the 99th percentile

Table 5: Regional Percentage Changes (1999-2007)

Regional   Provincial   GDP Per Capita %     Rural Household Income
Averages   Averages     Change (1999-2007)   % Change (1999-2007)

Eastern                 189.6                85.9
Central                 191.1                73.5
Western                 212.4                82.5
           Gansu        182.1                64.8
           Ningxia      238.5                77.6
           Shaanxi      275.1                81.6
National                199.3                80.1

Regional   Provincial   Secondary Education    Primary Industry
Averages   Averages     % Change (1999-2007)   % Change (1999-2007)

Eastern                 26.6                   -33.5
Central                 28.8                   -17.0
Western                 44.9                   -16.4
           Gansu        66.9                   -7.6
           Ningxia      26.7                   -21.9
           Shaanxi      41.7                   -15.0
National                34.7                   -20.7

Regional   Provincial   Secondary Industry     Agricultural Loans
Averages   Averages     % Change (1999-2007)   % Change (1999-2007)

Eastern                 34.6                   16.5
Central                 33.9                   70.7
Western                 27.9                   43.4
           Gansu        1.1                    28.1
           Ningxia      32.3                   151.2
           Shaanxi      16.1                   62.9
National                31.7                   46.7

Regional   Provincial   Fixed Asset Investment--   Exports--%
Averages   Averages     % Change (1999-2007)       Change (1999-2007)

Eastern                 378.8                      591.3
Central                 453.6                      556.9
Western                 429.7                      419.1
           Gansu        266.8                      423.4
           Ningxia      426.2                      678.3
           Shaanxi      1287.4                     654.6
National                427.9                      510.1

Regional   Provincial   Highways/Population    Irrigated Areas--
Averages   Averages     % Change (1999-2007)   % Change (1999-2007)

Eastern                 148.5                  1.7
Central                 203.4                  10.4
Western                 151.4                  8.4
           Gansu        170.0                  9.3
           Ningxia      83.4                   7.4
           Shaanxi      171.0                  -1.7
National                151.8                  7.5

Regional   Provincial   Population %
Averages   Averages     Change (1999-2007)

Eastern                 10.9
Central                 1.0
Western                 5.2
           Gansu        2.9
           Ningxia      12.3
           Shaanxi      3.6
National                5.1
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Article Details
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Author:Harrison, Isa; Houck, Meredith; Jiwani, Naushin; Mack, Richard; Welch, Jennie
Publication:East-West Connections: Review of Asian Studies
Article Type:Report
Geographic Code:9CHIN
Date:Jan 1, 2011
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