The impact of the great western development strategy on three provinces of Northwestern China.
Harrison, Isa ; Houck, Meredith ; Jiwani, Naushin 等
Introduction
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.
Background
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.
[FIGURE 1 OMITTED]
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.
[FIGURE 2 OMITTED]
The location quotient on road density is represented in Figure 3.
[FIGURE 3 OMITTED]
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.
[FIGURE 4 OMITTED]
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.
Objective
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.
Methodology
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.
Findings
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
Capita
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
Investment
Table 2: Regression Analysis Equations
Equation Equation
Number
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
Where:
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
Infrastructure;
Positively Positively
Electrical Industry Shifts; Crop Shifts;
Infrastructure Positively Positively
Water Industry Shifts; Crop Shifts;
Infrastructure Negatively Positively
Income Afforestation; Water
Negatively Infrastructure;
Positively
Microcredit
Education Migration;
Positively
Road Education; Health Services;
Infrastructure Positively Positively
Industry Shifts Migration; Positively
Road Infrastructure;
Positively
Electrical Water Infrastructure;
Infrastructure Positively
Water
Infrastructure
Income Migration; Minimum
Income Required
Microcredit
Education
Road Migration;
Infrastructure Positively
Industry Shifts
Electrical
Infrastructure
Water
Infrastructure
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
Population--1999
GWDS Presence
2) Rural Income/ 0.208 0.095
Household Rural Income /
Household--1999**
Population--1999
GWDS Presence
3) Highway Length 0.198 0.084
Highway Length--1999
Population--1999
GWDS Presence
4) Secondary 0.223 0.152
Education Population--1999*
GWDS Presence**
5) Agricultural 0.102 -0.026
Loans Agricultural Loans--1999
Population--1999
GWDS Presence
6) Primary 0.477 0.404
Industry Primary Industry--1999
Population--1999***
GWDS Presence
7) Secondary 0.431 0.349
Industry Secondary Industry--1999**
Population--1999**
GWDS Presence
8) Irrigated 0.267 0.162
Area Irrigated Area--1999
Population--1999**
GWDS Presence
9) Fixed Asset 0.155 0.034
Investment F.A. Investment--1999*
Population--1999
GWDS Presence
Model Standard Coefficient T-Stat P-Value
Error
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
Average
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
Average
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
Average
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
Average
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
Average
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
Average
