Master Master Master
Master Thesis Thesis Thesis Thesis of of of of Int. Int. Int. Int. Bus&Man Bus&Man Bus&Man Bus&Man
Analysis
Analysis Analysis Analysis of of of of the the the the Influential Influential Influential Influential factors factors factors factors of of of of FDI FDI FDI FDI Inflow Inflow Inflow Inflow in in in in China China China China
By Zhuyun Zhou S1823825 Supervised by Mr. F. Becker-Ritterspach.
Mr. R. de Vries
Abstract Abstract Abstract Abstract
Since the reform in 1978, China has kept a high speed of development for almost two decades. Among all the driving forces, FDI is considered as the most important one. With lower labour cost, huge consuming market and enormous potential, China is now the most attractive place for the investors in the world. But the difference among provinces and cities caused an imbalance of the FDI inflow. Correspondingly, the performance among provinces is also diverse a lot. According to the analysis in my thesis, the main reasons for gaps come from three aspects including geography, economy, humane, etc. The results also give some idea in improving development and reducing the gaps between areas.
Keywords:
Keywords:
Keywords:
Keywords: FDI, China, Comparative advantage, Economic geography
What What What What is is is is the the the the Key Key Key Key Factor Factor Factor Factor of of of of FDI FDI FDI FDI Inflow Inflow Inflow Inflow in in in in China China China China
1.
1.
1.
1. introduction introduction introduction introduction
China is becoming one of the most attractive emerging markets in the world. In the last two decades, the growth rate of China keeps at a very high speed of development which is almost twice the rest of the world (Zheng, 2009). This high increase in growth rate has resulted in a huge consumer demand for the resources and products based on the low cost of labour force and large number of population. In the process of the emerging, foreign direct investment (FDI) plays a very important role. On the one hand, the investment comes from other countries and regions have provided a very strong support for the economic booming. “FDI occurs not because of cost-of-capital differences but because certain domestic assets are worth more under foreign control. “(Kenneth, 1991). This means that the capital could work more efficiently when the foreign investors controlled it. According to the data from United Nations, there is a similar trend between the growth of FDI and the Change of GDP per capita since the reform of China in 1979 till 2006. FDI could be taken as a package of capital, technology and managerial skills, and is often viewed as an important source of both direct capital inputs and technology spillovers. (Balasubramanyam, 1996) Several studies have analyzed the role of FDI inflows on China’s output and export growth.
The importance of FDI inflow differed with the countries. For developing countries, FDI plays a more significant role than for developed countries.
Besides the foreign capital and the potential job opportunities, FDI also
transfers advanced technologies, know-how and managerial skills, which may
be amplified through spillover effects (Wang and etc., 2007). Past empirical
studies that inward have indicated that FDI has been an important driving
force for China’s remarkable 9% annual average economic growth over the
last two decades. Whether a particular country can successfully get great amounts of FDI is highly dependent on its economic, political and natural environment. At political level, the reform of Chinese government since 1978 was opened the domestic market to the foreign investors. The huge amounts of potential consumption in the country with more than 1 billion population show great attraction to all the countries with extra capital. Nowadays, the amount of FDI is more than one million times than what it was in 1979 (China Statistic year book, 2008). “FDI, as an economic growth agent, is more effective in outward export-oriented countries than in inward import-
substituting countries.” (Balasubramanyam, Salisu & Sapsford, 1996; Ozawa, 1992) China is exactly one the country which follows this way. With its huge population, the labour cost in China is relatively lower than in most countries with a similar development. The manufacturing cost in China is only 4% of the index in America. The import of raw materials and export of finished goods has been the main approach of most Chinese factories. It is quite a special style of development that the main improvements in every aspect are based on FDI inflow. The main idea of this thesis is to make clear the mechanism inside the whole process and try to find out some useful idea for this style of
development.
---Table 1.1---
In order to find out the determinants of the FDI inflow, I reviewed some of the
data in the history. The indicators in the field of FDI inflow differed widely
among provinces and cities. Different attraction of the areas to the investor
draws a total different result. As is shown in table 1.1, the utilization of foreign
direct investment by most of the provinces and cities in 2005 could. According
to the official division of geographic location in China, I add some notes
following the name of provinces and cities which represent the location (w for
western China and e for eastern China). It is obvious to see that the eastern
provinces and cities occupied almost the first half of the table and the western provinces only appear since the 17
thposition. But why is there so huge
disparity between the provinces? The surprising comparison makes it
necessary to introduce the situation of western and eastern China. Generally speaking, the western part of China is inland and most of the places are on the plateau and Mountainous is the main landform of these parts. Relatively, the eastern parts are quite flat and the provinces locate along the costal line of China. According to the statistic information, 70% of the total territory of China only shares approximately 10% of the total FDI and the rest 90% FDI is also distributed uneven among the eastern parts. Judged from all the data and numbers above, I roughly draw an assumption that “In China, the FDI inflow would follow the geographic location. The eastern parts are better than the western parts in the attraction of investment.” But when I checked the table 1.1, I found some exceptions. Anhui province, for example, which located in the eastern parts, has a quite poor performance and only shares 0.75% FDI inflow. The amount is much lesser than its neighbours, Shanghai or Shandong.
The exception makes my assumption become quite uncertain.
On the practical level, the different number in the table also caused some problems in the real world. The imbalance in the distribution of FDI resulted in quite a lot of problems. The economic distance is the main reason which caused the population flow among cities. (Williamson, 1965) For one side, the accumulation of the capital makes some of the cities become larger and larger.
A large population costs a huge amount of resources and caused quite a lot of problems that only happened in huge cities. As described in Ögütçü (2002),
“China's largest and fastest-growing energy demand is in the southern and
eastern coastal provinces, but its energy endowments are located in the North
and West. These geographical facts have reinforced decentralization trends in
China’s economy and shape its energy options. Moreover, “the outflow of the
labour power and resources from some less developed area worsen the
economic environment of the area (Tsui, 1991). The theory shows a very simple circulation in the economic development. The first improvements of economy in a certain place attracted the people around it and make use of the resources which belong to the other area (Migrant labour, for example). As more resources are used, there will be an even greater development but for the area which outflow the resources, the change will be even harder and these areas are highly possible to stay poor. In the long run of the circulation, the gaps between rich and poor will be larger and larger.
To make a brief conclude, many questions which come from the data should be answered. In my thesis, I would like to make an analysis on the factors that may affect the investment inflow. I will start from the theory review and try to find out the possible explanation for the FDI inflow in former theory. I will analyze the FDI inflow in China by utilizing the possible variables that find out from the former theory to the Chinese situation to test the applicability of the theory on national level. The main question I will answer in my thesis is:
What are determinants that influence the FDI inflow to different regions (or states/cities) in China?
2. 2.
2.
2. Theoretical Theoretical Theoretical Theoretical Background Background Background Background
The theory of comparative advantage gives some basic ideas about how
investment flows from one area to another. This theory gives a reasonable
explanation for the huge amounts of FDI inflow into China. The main reason is
obviously the low cost of labour. But among the cities, there are not so many
differences. According to Zhang (2001), “The reasons for the concentration of
trade and foreign investment in the coastal area are its inherent comparative
advantage in terms of lower labour cost, better infrastructure facilities, close
relations with overseas Chinese, favourable geographic location, as well as
national industrial policies that protect the domestic market from foreign investment.” Harry Jonson (1970) pointed out that “countries with different economic size, different institutions for education of people and support for the knowledge” should also be taken into consideration the countries comparative advantages. Dollar (2001) points out that “technological advance plays an important role in developing nations’ growth and evolving comparative advantage.”
It is hard to find out the relationship between FDI inflow and the properties of a certain area. When I tried to make use of some basic index of provinces and cities to explain the question I just raised, it is hard to draw a clear relation. For example, in table 2.1, there shows the salary level of the main cities in China.
Among all the cities, Beijing and Chongqing have similar scale in population, but totally different level of salary. According to the explanation of the labour cost theory, Chongqing should have more FDI inflow than Beijing. Also the similar doubt appears when considering Lhasa. Because of the imbalance of China’s population, in some of the area higher salary does not mean the higher development and lower labour cost is not necessarily the influential factor of FDI inflow. Some of the theories that applied in worldwide are not quite persuasive in explaining the problems.
In table 2.2, the transaction value in technology market could represent the
level of technology in each city or province. The result corresponds to the
theory because the cities with better technological level always perform better
than the others. But still, there are some exceptions such as Fujian. The low
output of technology did not affect the performance of this province in the FDI
area. The fact shows that low cost of labour and higher levels of technology
did not play the only key role in the process. More variables that concerned
other aspects should be taken into consideration.
---Table 2.1--- ---Table 2.2---
The theory of economic geography has thrown light on the problem.
Boudeville introduced the polarization concept by improving the Pole theory raised by Perroux. He defined a polarized region as “a set of neighbouring towns exchanging more with the regional metropolis than with other cities of the same order in the nation”. (Boudeville, 1976; Althoff, 1977) In China, the first opening markets in the process of reform in 1978 only contained several coastal cities and provinces; most of the multinational companies chose big cities such as Beijing or Shanghai as the location of their headquarters.
Nowadays, Shanghai contains the second largest number of headquarters of companies in China, less than the numbers in Hong Kong. As the pole in the theory, many of the cities grew up around a metropolis such as Shanghai, Beijing and Guangzhou. These cities grew into the Yangtze River Delta economic circle city, the Pearl River Delta economic circle, and Bohai-rim Delta economic circle. The three economic circle make up a costal economic.
As for inland China, the opening of these cities was far later than the costal cities. There is no real metropolis in the western part of China and big cities with huge amounts of population do not have as much attraction as ones in the costal areas because of the distance to headquarters of the companies. As one of the most important FDI inflow in China, the investments from multinational companies are very important and the result of attraction will always result in a high inflow of GDP.
Krugman (1991) gives further explanation of the accumulation of investment in
a micro view. “The manufacturer productions will tend to concentrate where
there is a large market, and the market will be even larger where
manufacturers’ production is concentrated.” The effect between the market
and the manufacturers is also called “positive feedback” (Arthur, 1990). China,
as an example, is a very huge market for investors. If the investor does not consider the scale of the market, “the tie of production to the distribution of land will be broken”. (Krugman, 1991) Moreover, the accumulation of companies results in a scale effect (Krugman, 1980) which brings them huge amounts of information and plenty of consumption. The entrance of the companies attracts more labour from the other district in the country. (Gong, 2007) In large countries, the expansion of company is risky and unpredictable because of the great difference among markets. To expand in a market with poor information is dangerous and unwise. By comparison, the market around a metropolis is relatively easier for them to enter. The markets are similar to the core market in a metropolis and there is enough information so that headquarter to control the risk acceptable level. Most companies tend to follow the style of expansion and choose the suitable location of the subsidiaries in areas around headquarter.
Most of the theories showed above seem trying to explain the phenomena concerned about FDI on country level and they do not give enough clues about the explanation in a further level. In my thesis, I would like to make a further step and analyze the problem on in-nation level. The basic idea is to make use of the existing theory and apply it in another circumstance (China in my thesis). Several differences could be expected before my analysis. To begin with, the country level communication is much harder than the in-nation level because of a lot of differences such as cultural and economical ones. But inside one country, the gaps are much lesser. When the investors try to make the decision, there is less consideration about these aspects. Second, the investment is easier to flow among cities in one area than among countries.
The barrier between countries does not exist between cities in the same
country. It makes the investment much more flexible. For example, the investor
could choose the place of the factory in the place with lower good price but
sell all the things in another city near it. But on country level, there is no such
choice. The investor should accept all the factors in one country. But there are still a lot of similarities between the two levels. Natural difference for example is different everywhere. In world level, there are countries with more resources and fertilized soil as well as the poor-resource countries. It is the same on the in-nation level. Another possible is concerned about the policy. As mentioned above, Chinese government has different policy in developing countries in costal area and in in-land area. Always policies in one country have great influence in cities development. There is no such force in world wide. It is the special characteristic of cities’ development. My thesis will based on the former theory and set hypotheses according to the country level but choose samples of cities. The research of applicability will be another purpose of my writing. By considering all the factors, several hypotheses could be drawn.
---Table 2.3---
In table 2.3, I draw a conclusion for the theory review I mentioned above. My ideas come from two theories. The first is comparative advantage theory. The main factors could be found include four aspects as far as I could see. They are the quality of infrastructures, the cost of labour, the economic size of the market and the technology level. Similarly, several factors could be found in geographic theory. I divide the theory into two parts because they are of some differences and Krugman’s theory contains some new concept in the field.
From the Table, the common factor is the market size. All the factors in comparative advantage theory only focus on the country level FDI flows and did not mention the in-nation level. But the factors are also possible to apply in analysis on in-nation level such as the economic level (evaluate by cities’
GDP), the quality of infrastructures. Some of the factors cannot apply directly
but it could be the reference by certain means. Geographic economy theory is
more microcosmic, but there is still not a lot of literature on it. In my review,
some of the articles are talking about the situation in China on a micro level, Li
(2004), for example, apply some of the factors in western China in the theory but did not give detailed analysis on it. Among all the theories I reviewed, the factors in the theory contains the resources, consumptive power and the location. In my thesis, I would like to choose some of the variables in the table.
Some of them are quantizable, but some of them are hard to measure. I will give thorough explanation on it.
3. 3.
3.
3. Hypothesis Hypothesis Hypothesis Hypothesis
In the process of FDI inflow, the choice of the investors comes from a combination of all the possible factors that may affect the result of investment.
In Loewendahl and Ertugal-Loewendahl (2001), the variables that used to describe the FDI problem contained every aspect from natural to social fields.
Some of the variables are also in the table I conclude. It is impossible to measure all the factors in my thesis because some of the variables are not measurable and some of the factors do not really exist on in-nation level. But it is valuable to make a comparison between the tables because I could choose some common variables as the indexes. By comparing Table 2.3 and Table 3.1,
I would like to set up my hypotheses from three aspects (economic, geographic and humane), which are listed as follows:
---Table 3.1---
3.1 3.1 3.1
3.1 Economic Economic Economic Economic environment environment environment environment
Economy is the most important aspect of one city when talking about
development. There are quite a lot of determinants in the theories that try to
explain relationship between the economic aspects and FDI inflow. But some
of them exist on country level (The performance of economy that influenced by
the cultural factors, for example). Similarly, some of the other factors, such as
the inflation, do not differ a lot among cities in the same country.
Taking the factors into consideration, I would like to choose GDP as the variables in my analysis. Although there are some different understandings of this index, “It is a fundamental measurement of production and is very often positively correlated with the standard of living. “(Sullivan &Steven, 1996) In general, higher GDP of a city means a better level in every aspect. There are more wealth belong to the residents which means more consumptive power and larger markets for expansion. In China, the evidence shows that foreign investment is positively correlated with export and GDP growth. (Claro, 2008) It is obvious to see that the increase of FDI has had a significant effect in boosting GDP. The fast climbing in 1991 resulted in a boom of Chinese economy until the Asian Financial Crisis. (See table 3.2 and Figure 1) Meanwhile, according to the comparative advantage theory, there should be a positive feedback to FDI inflow from the increasing GDP. So the hypothesis will be drawn as:
Hypothesis 1: FDI inflow in a city will be positively affected by the GDP of the city.
3.2 3.2 3.2
3.2 Geographic Geographic Geographic Geographic influence influence influence influence
In the theory of geographic economy, a lot of factors have been mentioned to be the factors in FDI performance. In general, the influence could be divided into two parts, the natural one and the man-made one. The natural aspect mainly focuses on the landform and the resources reserve in a certain area.
But it is hard to make a thorough consideration of the landform because the
influence is hard to be quantized and it differed with the cities. In conclude, it is
hard to define the positive and negative aspects. In Loewendahl and Ertugal-
Loewendahl (2001), there is not landform variable either. But what should be
mentioned is another aspect in the natural geography cannot be neglected.
The resources are always very important to one country or one area. The meaning of resources is a kind of “talent” of a country or area. It will provide the power of developing in the long run. So it is reasonable to say that rich resources will be very helpful in the process of development. So my second hypothesis which based on the expectation is:
Hypothesis 2: FDI inflow in a city will be positive affected by the richness of resources in the district.
In old time, the unchangeable and unmovable resources would always lead to a centre of development. For example, the prosperity of the west coast of America comes from the gold mine. But with the development of the
technology and science, “unmovable and unchangeable” are no longer the properties of natural resources. By developed net of transportation, cities which are far from the resources could also acquire the resources with ease. In the former theories, there are also similar findings. Li (2004) mentioned that
“under the circumstance of the separation of production factors and the
production place, the cost of transportation and convenience became the most important factor for the investor” (My translation). Similarly, in table 3.1., there are also such factors which called efficiency seeking in the list of influential factors. In order to make the factor measurable, I would like to choose transportation as a representation of this aspect. Therefore, the third hypothesis is:
Hypothesis 3: FDI inflow in a city will be positive affected by the ability of transportation of a city.
3.3 3.3
3.3 3.3 Humane Humane Humane Humane aspects aspects aspects aspects
Despite the aspects I mentioned above, humane is another factor which worth being thought. I would like to contribute the factors into the aspect which is called the humane aspects. It is not totally objective factors but it plays as important a role as the others. Among all of them, population is the most important one. Generally speaking, population is the basic motivation of development. More population means the potential labour power and huge demand for products. Most of the time, population distribution was affected by the policies and the environments of an area. In China, the costal areas with more plains and less mountains are of higher population. Meanwhile, the areas are also the place with more FDI inflow and investments. Whether it is a coincidence or inevitable outcome is really worth considering. So, my next hypothesis was set up to test the relationship between them.
Hypothesis 4: FDI inflow in a city will be positive affected by the population of a city.
There is no doubt that population is the source of labour. But the biggest motivation of development is the skilled worker which is very efficient when finishing a hard work and easy to be trained when needed. There is no doubt that the way to train skilled worker is education. As Greenspan (2007) pointed out “Enhancing elementary and secondary school sensitivity to market forces should help restore the balance between the demand for and the supply of skil led workers in the United States”. Of course, it is the same in other countries.
So I would like to draw a rough conclusion that the better basic education environment an area has the better human resources the area will acquire and the faster development will be in the area. And my last hypothesis comes out as:
Hypothesis 5: FDI inflow in a city will be positive affected by the education
level of it.
Table 3.2 Hypothesis in this part
4.
4.
4.
4. Data Data Data Data and and and and Methodology Methodology Methodology Methodology 4.1
4.1 4.1
4.1 Measures Measures Measures Measures
The purpose of the analysis is to make sure what kind of factors play the most important role in the process of FDI inflow. The main idea of analysis is to find out the relationship between the influential factors and the level of development of a city by using correlations analysis. The data in the analysis include the indexes of 31 provinces or cities in China in year 2007. All the indexes are chosen according to the hypotheses I set above. To make the analyses quantizable, I find out several indexes to represent the factors.
Variables in the Analysis include two parts, the dependent variable and the independent variables.
4.2 4.2 4.2
4.2 Variables Variables Variables Variables
The dependent variable in the model is the FDI inflow in every province. And the independent variable in the model will be set to represent the factor in the hypotheses. For Hypothesis 1, as Table 4.1 showed, is the GDP per capita. It is the main index to evaluate the resident’s living condition and the level of development of a city. I got the value by using population divided the total GDP
Factors in Hypothesis Expected Relationship
GDP Positive
Resources reserve Positive
Transportation ability Positive
Population Positive
Educational level Positive
one city has. All the data are come from the Chinese City Statistic year book.
---Table 4.1---
Table 4.2 is the general situation of the resource reserve in China. I choose three kinds of resources from all the resources which may play the most important role in industry. As it is hard to make all kinds of resources together, I plan to analyze them separately.
---Table 4.2---
Table 4.3 contains the data about land use in every province or city. The two indexes are construction use and the transportation use. I got the value of them and I made a simple calculation to translate them into the form of percentage which will be helpful in comparing the transportation facilities in a city.
---Table 4.3---
Table 4.4 is mainly use to explain the influence of the population to the FDI.
Except the total population of the province and the city, I will also use the proportion of the urban one as the index. The reason why I would like to make a further discussion is that the urban population always has greater power in consumption and will be more helpful in the economic development. The test will be nice make clear of the common concept.
---Table 4.4---
Table 4.5 tells the educational level of the cities and provinces. I only choose
two of all the levels because the two levels are relative more important. Higher
education will shape the people into a skilled worker in one field. Also it is the
best way to increase the quality of the workers. The reason why I would like to test senior high school level is that I would like to set a comparison for higher education and makes the result more persuasive. All the data I will use is the statistic data in year 2007 come from the statistic yearbook of Chinese government.
---Table 4.5---
5. 5.
5.
5. Analysis Analysis Analysis Analysis and and and and Result Result Result Result
5.1 5.1
5.1 5.1 Correlation Correlation Correlation Correlation
The correlations between independent and dependent variables are listed in table 5.1. Among all the factors in the hypotheses, GDP per capita shows the significant positive relation with FDI inflow as expected. It means that the investors will consider the development of the city. Cities with health economy will be more attractive in FDI inflow. So So So So H H H Hypothesis ypothesis ypothesis ypothesis 1 1 1 1 is is is is a a a a true true true true proposition proposition proposition proposition.
---Table 5.1---
Another significant positive relationship is the population. The more population one city has, the more FDI inflow will be. Also the percentage of urban population seems to be the same trend in the process. More citizens in the city will provide not only the low cost of labour, huge power of consumption, but also large markets for expansion. Hypothesis Hypothesis Hypothesis Hypothesis 4 4 4 4 is is is is also also also also supported supported supported supported by the facts and we got the result that “In China, population will be positively affecting the FDI inflow.”
Hypothesis Hypothesis Hypothesis
Hypothesis 2 2 2 2 did did did did not not not not get get get get support support support support from from from from the the the the analysis analysis analysis analysis. On the contrary, the resources seem to play a very minor effect on the cities’ development.
Resources that reserve in the area will not give enough support for the
development of an area. It is quite different from what I expected. It is hard to
find out the answer for the difference between data and expectation. But possible guess could be drawn from Hypothesis Hypothesis Hypothesis Hypothesis 3 3 3 3 which which which which is is is is highly highly highly highly positively positively positively positively related
related related
related with with with with FDI FDI FDI FDI inflow inflow inflow inflow. Possible explanation for Hypothesis 2 could be the result of transportation cost and the negative side of resource exploiting. By means of modern network of transportation, transfer of resources from one place to another will no longer be a difficult problem. Lower cost and efficiency on transportation will largely reduce the importance of the location of natural resources. Meanwhile, the division of industries also changed some cities into the one with multi-function. Shanghai, for example, is the city with developed finance as well as the heavy industries. But it does not have any natural resources reserved. The main reason is the highly concentration of manufacturing and services. With higher add value, the third industry could provide more outcomes to make up for the cost on transportation. Furthermore, the developed transportation network and advantage location makes it possible to get the resources that are needed with relative low cost. Meanwhile,
the style enables Shanghai to become a city which could focus on the import and export. Just as the statistic showed, the primary and secondary industry which is highly dependent on resources plays a very minor role in such kinds of modern cities. As Table 5.2 shows, similar cities (such as Beijing) are also highly count on the Tertiary industry which mainly provides services. In some even modern cities in the developed countries, the tertiary industry takes almost all the share of the industry and support the whole city independently.
Last but not least, economic geography plays more effective than the
traditional way of thoughts when comparing the area in a similar background. In
the analysis, some of the uncontrollable variables (such as the policy, laws)
are neglected by me because they are hard to evaluate and the influence could
not be expected before they come into being. Some provinces which have
great amount resources are also the area with shortest open period to the
foreign investment. If considering this factor, none of the theories I mentioned
could be efficient in explaining the phenomena.
---Table 5.2---
The influences from educational level are mainly showed in the last two indexes. The result tells that both of the ratios of basic education and higher education have positive influence to FDI inflow but the significance is not enough to give enough support for the judgment. So, Hypothesis Hypothesis Hypothesis Hypothesis 5 5 5 5 is is is is not not not not fully
fully fully
fully supported supported supported supported by by by by the the the the analysis analysis analysis analysis but I could not deny it as well because the digits really show a positive trends. The possible explanation for the lack of evidences is the shortage of data. But we could still get reasonable understanding for the relationship because good education quality in a certain area will surely result in a great number of skilled workers. The positive feedback will play very important role. Skilled workers are considered as a very important determinant in cities development. Meanwhile, the modern cities are also attractive to the competitive people. Just as the facts in China, the provinces that own many excellent universities such as Guangdong, Beijing and Shanghai, are exactly the very developed provinces and cities.
6. 6.
6. 6. Conclusion Conclusion Conclusion Conclusion and and and and Limitation Limitation Limitation Limitation 6.1 6.1
6.1 6.1 Conclusion Conclusion Conclusion Conclusion
The result showed the applicable test of all the factors in in-nation level. Some of the factors perform as expected such as the facilities level and the educational level. But some of the factors do not perform the same. Resources,
for example, perform totally contrary to the expectation. Rich resources sometimes will be harmful to the cities development. By considering the role that transportation facilities play, it is obvious that a good transportation system will be more valuable. The resources are easy to be distributed again in nation.
Also, the problems that exploitation caused will keep a lot of investment away
from the city because the system of the cities is not health enough.
Based on all the data and analysis above, I would like to give an answer to my main question that I proposed. The determinants of FDI inflow is a mixture of a lot of factors. The factors include the factors I mentioned such as geography, economy and humane as well as some factors that I didn’t expected. All the factors play a more or less important role in the process of FDI inflow.
According to the result of my analysis, the geographic factor is very influential in the process. It is reasonable to say that western provinces are largely impeded by the natural environments. First, most of the main difficulties come from the mountainous landform. Just as I mentioned above, the huge amounts of resources do not give enough support for the economic development because of the poor transportation. Distance to the producer not only reduce the advantage of the provinces, but also add a lot of burden on these area because the exploit of resources always results in a great cost on environment Second, the way of resource-oriented is not efficient enough. By comparing the industry of a city or province, it is easy to know that the high-add-value industry have become the mainstream of a modern area. How to make good use of the resources plays more important roles in than how many resources one area has. Third, the education level is very important. Quality of the labour power is the key factor in human resource. High quality labour power could create much more wealth than same amounts of low quality labour.
6.2 6.2 6.2
6.2 Implication Implication Implication Implication
With all the conclusions above, I could try to answer the questions that appear
in the introduction parts. The first one is why Anhui province performs totally
different from the provinces around. The reasons may come from several
aspects. First of all, there are almost all the landforms in this province. There
are many mountains in this province and some of them are still
underdeveloped because they are too far from the centre of the city. Also,
there is a very low percent of urban residents. According to the table above,
this percentage of Anhui province is not in accordance with the other provinces in the eastern part of China. On the contrary, the percentage is quite close to the western part of China. The general structure of this province seems to be following the western parts instead of the eastern parts.
Another question is about the reduction of gaps between rich and poor. As the FDI takes the responsibility of the gaps between the west and the east, the more FDI that one province could introduce, the better development one province will have. Based on the key factors of the eastern cities, it is predictable that the costal provinces will still keep the steps of improvements because there are good supports from every aspect. I suppose that it is almost impossible for the western provinces to catch up with the eastern ones in the next few years because of the huge gaps existing now.
China is one of the fastest and biggest developing countries in the world. The fast development of the countries benefits the citizens a lot and the level of living is improving in every aspect. Among all the motivation of the development, FDI is the most crucial one in the economic area. The inflow of investment gives a lot of capital in production as well as the technology that exchanged in the process. But what is happening is that the FDI is not evenly distributed in different area. In some of the cities or provinces of China, there is no investment at all. The difference among cities and provinces will make a lot of problems in the long run. The gaps between cities will be bigger and bigger wealth accumulation will result in the distance between rich and poor. My thesis is mainly focus on the problem and tries to find out the key factors in the process of FDI inflow in order to know how to improve the situation now. The test also showed that most of the factors that will influence the country level FDI will also influence the in-nation level.
In practical situations, the most direct factor in the FDI inflow is the economy of
the city. Higher GDP will increase the competitive power of countries or area.
The great increasing of Chinese FDI is the best example. In China, some cities do have good potential of economic environment such as some undeveloped cities in the costal area. They should make use of the resources around and try to follow the steps of the other modern cites and district. But in some area in the west, the cities just lost the chance of attracting investment at the start of the reform because of the policy aspect. It is quite hard fir then to solve the problem themselves. So the central government should try to give support to these cities. The tax relief policy will be a very effective way. But the method is highly relying on the decision of the government, it is not expectable. So it is not an active solution of the cities. Even though, there are still some methods that could be considered.
For the first, the structure of industry in some backward area should be changed. The provinces that have a lot of resource-oriented enterprises have an excessive reliance on the natural resource and make profit only by exporting the resources to the other area in nation or around the world. With the development of the country, the style will be less effective in improving the economy. Also it will cause quite a lot of problem at the same time such as the waste of resources and the pollution. What’s more, the cities with rich resource will have less motivation to change the situation because the start of changing will be quite hard. But the gradual change is a must because the resources are always limited and it is not the way of sustainable development. The good structure of industry will result in a health economy and reduce the consumption of reliance on natural resource.
Second, the improvement of the infrastructure should always be the
considered as the important task. In one aspect, better transportation means
less cost in import and export. All the industries will benefit from the
improvement. In another aspect, better transportation will promote the
communication among cities around. The more information the cities share with the other cities, the more chance that the investment will flow into the city.
6.3 6.3 6.3
6.3 Limitation Limitation Limitation Limitation
FDI could be a very comprehensive index. There are a lot of indexes that will have influences on it. In my thesis, I choose some of the factors which are related with the available theories. But I haven’t considered the collinearity between them. I tried to make regression analysis on the data during the process but they do not seem to be good fit with the data. Another shortage comes from the quantity of the data. I only choose one year’s data, they could tell us some facts but I really think that they are not enough for explaining the problem. Moreover, I neglect the influence of some key factors such as the policy and law. They always have greater influence in China but it is hard to evaluate the power of them.
Reference Reference Reference Reference Book Book Book Book
Hardcover (1975), Independent Development,
English English English
English Journal Journal Journal Journal
Alan Greenspan (2007), the Age of Turbulence: Adventures in a New World, p.
405, the Penguin Press, 2007 ISBN 978-1-59420-131-8
Arthur, W.Brian (1989). “Competing Technologies, Increasing Returns and Lock-in by Historical Events.” Econ.J.99 (March 1989):116-31
Balasubramanyam, V.N, M. Salisu, and D.Sapsford. (1996). FDI and Growth in
EP and IS countries, The Economic Journal 106, 92-105
Chengang Wang, Yingqi Wei and Xiaming Liu (2007). Does China rival its neighbouring economies for inward FDI? , Translational Corporations, Vol. 16, No. 3
David Dollar (1991), “Technological Differences as a source of comparative advantage”, APE Paper and Proceedings, May 1993
Harry Jonson (1970), “The State of Theory in Relation to the Empirical Analysis,” in Raymond Vernon, ed., the Technology Factor in International Trade, New York: Columbia University Press, 1970, pp 9-21
Kenneth A. Froot and Jeremy C. Stein (1991), Exchange Rates and Foreign Direct Investment: An Imperfect Capital Markets Approach, the Quarterly Journal of Economics, Vol. 106, No. 4 (Nov., 1991), pp. 1191-1217
Loewendahl and Ertugal-Loewendahl (2001), Turkey's performance in Attracting Foreign Direct Investment, European Network of Economic Policy Research Institutes Working Paper No. 8/November 2001
Mehmet Ögütçü, “Foreign Direct Investment and Importance of the “Go West”
Strategy in China’s Energy Sector”
Ozawa. T (1992), “Foreign direct investment and economic development“, Translational Corporations, (1), 27–54.
Paul Krugman (1980), “Scale Economics, Product Differentiation, and Pattern of Trade” A.E.R 70 (December 1980):950-59
Paul Krugman (1991), Increase Returns and Economic Geography, Journal of
Political Economy 1991, vol99, no.3
Phillip Althoff (1977), Reviewed work(s): Interdependent Development by Harold Brookfield, The American Political Science Review, Vol. 71, No. 3 (Sep., 1977), pp. 1214-1215
Ping Zheng (2009), a comparison of FDI Determinants in China and India, Published online in Wiley InterScience (www.interscience.wiley.com)
Tsui，Kai Yuen (1991)，"China's Regional Inequality：1952-19985"[J]，Journal of Comparative Economics，1991：15：1-21
Sebastián Claro (2008), FDI Liberalization as a Source of Comparative Advantage in China, Review of development economics, vol. 13 (2009), issue 4, page 740-753 (14)
Sullivan. Arthur, Steven M. Sheffrin (1996). “Economics: Principles in action”, Upper Saddle River, New Jersey 074589: Pearson Prentice Hall. pp. 57, 305. I SBN 0-13-063085-3.
Williamson, J.G. (1965), Regional Inequality and the Process of National Development: A Description of the Patterns [J], Economic Development and Cultural Change, 1965, Vol.13, No.4, pp.3-45
Zhang Wei (2001), “Rethinking Regional Disparity in China”, Economics of Planning 34: 113–138, 2001
Chinese Journal
Li Xiaojian, Regional Change of Foreign Direct Investment and Strategies for Attracting Investment in Inland China, Economic Geography, May 2004, vol.25
Gong Jiayou (2007), Effect analysis on regional employment structure of Chinese FDI, Zhongguo Jingji Pinglun June 2007, vol 6
Internet
Jin Runcheng, Zhang Junfang,Liu junde (2004), The Principles of New Urbanism for Community Planning and Design, Economic Geography Vol24
China Statistic Yearbook 2007, www.stats.gov.cn
China City Statistic Yearbook 2006, National bureau of Statistics of China
Appendix Appendix Appendix Appendix
Figure 1 Comparison between GDP per capita and FDI in China 1979-2005
0 10000000000 20000000000 30000000000 40000000000 50000000000 60000000000 70000000000 80000000000 90000000000
1979 1982 1985 1988 1991 1994 1997 2000 2003 0 1000 2000 3000 4000 5000 6000 7000 8000
FDI (Million USD) GDP per Capita
Source: China statistic yearbook
Table 1.1 Utilization of FDI inflows by region in China 2004 (100million USD)
Resource China Statistic Yearbook
Rank Province/City Utilization of FDI Percent
1 Jiangsu(e) 12.1 16.39%
2 Guangdong(e) 10.012 13.56%
3 Shandong(e) 8.7 11.79%
4 Zhejiang(e) 6.68 9.05%
5 Shanghai(e) 6.541 8.86%
6 Liaoning(e) 5.41 7.33%
7 Fujian(e) 5.318 7.20%
8 Beijing(e) 3.08 4.17%
9 Tianjin(e) 2.472 3.35%
10 Hubei(e) 2.071 2.81%
11 Jiangxi(e) 2.05 2.78%
12 Hebei(e) 1.62 2.19%
13 Hunan(e) 1.418 1.92%
14 Heilongjiang(e) 1.24 1.68%
15 Henan(e) 0.874 1.2%
16 Hainan(e) 0.643 0.87%
17 Neimenggu(w) 0.627 0.85%
18 Anhui(e) 0.55 0.75%
19 Shanxi(w) 0.527 0.71%
20 Jilin(e) 0.453 0.61%
21 Chongqing(w) 0.405 0.55%
22 Guangxi(w) 0.296 0.40%
23 Sichuan(w) 0.225 0.30%
24 Yunnan(w) 0.142 0.19%
25 Ningxia(w) 0.13 0.18%
26 Shanxi(w) 0.09 0.12%
27 Guizhou(w) 0.065 0.09%
28 Xinjiang(w) 0.046 0.06%
29 Gansu(w) 0.035 0.05%
Total 73.82
Table 2.1 Salary and Population in Main city of China (2008)
Resource China Statistic Yearbook
City Population Salary of Staff and
Workers City Population Salary of Staff
and Workers
(10 000 persons) (Yuan) (10 000 persons) (Yuan)
Shanghai 1379 49311 Chengdu 1112 26607
Lhasa 62 46954 Hefei 479 25874
Beijing 1213 46508 Haikou 153 25723
Guangzhou 773 40561 Wuhan 828 25136
Shenzhen 212 38797 Xi'an 764 25014
Hangzhou 672 36497 Nanning 684 24791
Nanjing 617 35907 Taiyuan 355 24688
Tianjin 959 34938
Changchun 746 24189
Ningbo 565 32924 Fuzhou 630 23950
Urumqi 231 29110 Nanchang 491 23887
Xiamen 167 28959 Xining 215 23333
Yinchuan 149 28604
Chongqing 3235 23097
Dalian 578 28271 Guiyang 360 22579
Changsha 637 27967 Kunming 518 22432
Shenyang 710 27372
Zhengzhou 707 22156
Qingdao 758 27084 Harbin 987 22104
Hohhot 221 26735 Lanzhou 319 21019
Jinan 605 26654
Shijiazhuang 955 19992
Table 2.2 Transaction Value in Technical Market by Region
RMB (Yuan )
Region
Region Region Region 2000 2001 2002 2003 2004 2005 2006 2007
National National National National Total Total Total
Total 6507519 6507519 6507519 6507519 7827489 7827489 7827489 7827489 8841713 8841713 8841713 8841713 10846728 10846728 10846728 10846728 13343630 13343630 13343630 13343630 15513694 15513694 15513694 15513694 18181813 18181813 18181813 18181813 22265261 22265261 22265261 22265261 Beijing 1402871 1910065 2211738 2653574 4249975 4895922 6973256 8825603 Shanghai 738952 1061603 1202170 1427790 1716963 2317328 3095095 3548877 Guangdong 482104 539722 684532 805730 572651 1124740 1070257 1328448
Abroad 732342 1044979
Liaoning 347817 408698 508326 620200 752817 865167 806494 929290 Jiangsu 449568 529165 594873 765163 897855 1008296 688297 784173 Tianjin 262581 306009 363262 420008 450276 507093 588624 723356 Hubei 276000 338597 348603 412538 461700 501823 444427 522146 Hunan 286833 293887 323422 369306 408280 417394 455281 460816 Zhejiang 276275 316652 389438 530353 581465 386954 399618 453474 Shandong 288135 321938 347650 525682 750850 983614 232005 450275 Chongqing 296594 289484 409433 555083 596186 357059 553479 395658 Heilongjian
g 152382 111035 120110 121165 125715 142585 156934 350209
Sichuan 104150 126311 77524 128686 165640 190823 259323 303878 Shaanxi 92560 84615 151554 168022 139129 188977 179485 301710
Anhui 61012 64145 75423 87960 90675 142553 184921 264515
Gansu 26413 27393 54644 77581 119608 172736 214534 262107
Henan 211621 212589 178506 192690 203207 263737 237288 261907 Jilin 71390 88543 82921 87292.3 107900 122261 153666 174845
Hebei 94143 46784 60406 67969 72718 103827 156099 164329
Fujian 172601 136941 128988 166779 141395 171959 113187 145579 Inner
Mongolia 60287 62359 58197 108452 104085 109939 107127 109835
Jiangxi 69299 62724 62891 83323 93661 111227 93135 99533
Yunnan 187742 255279 179496 228718 215555 159175 82747 97496
Shanxi 5258 14693 39014 32251 59960 47980 59213 82677
Xinjiang 66168 82387 100699 120395 133371 80029 76084 71724
Qinghai 4657 12373 8291 12793 11812 24665 53017
Hong Kong, 15557 24308
Source: China statistic yearbook Macao and
Taiwan
Guangxi 17741 37753 44406 41808 90955 94059 9423 9970
Hainan 83990 9134 11978 1885 10007 8535 7327
Ningxia 6402 8872 8496 10047 12827 14131 5349 6641
Guizhou 620 599 13484 17892 13533 10488 5361 6560
Tibet
Table 2.3 Theory Review
Theory Factors Possible Variables could be drawn
Comparative advantage Quality of infrastructures Investment in cities facilities
Cost of Labours Labour cost
Economic Size GDP, GDP (per capita),etc.
Technology Level Cost on research, Education, etc.
Geographic Economy (Boudeville, etc) Concentration of the cities Population in certain area, number of big cities around metropolis
Resources Resource reserve, resource used
Geographic Economy (Krugman) Scale of markets Population, GDP per capita, urban percent,etc Amounts of consumers
Information exchange Communication facilities
Table 3.1 Strategic and project determinants of country attractiveness for FDI
Source: Loewendahl and Ertugal-Loewendahl (2001)
Table 3.2 FDI of China and GDP per capita (PPPs)
Resource: UNdata
http://data.un.org/Data.aspx?q=gdp+china&d=CDB&f=srID:29921;crID:156 Year Value(Million USD) GDP per capita (PPPS)
2005 79126731413 6760
2004 54936483255 5993
2003 47076719000 5336
2002 49307976629 4784
2001 44241000000 4338
2000 38399300000 3940
1999 38753000000 3583
1998 43751000000 3313
1997 44237000000 3069
1996 40180000000 2790
1995 35849200000 2515
1994 33787000000 2247
1993 27515000000 1968
1992 11156000000 1707
1991 4366000000 1479
1990 3487000000 1327
1989 3393000000 1249
1988 3194000000 1174
1987 2314000000 1036
1986 1875000000 918
1985 1659000000 838
1984 1258000000 726
1983 636000000 615
1982 430000000 542
1981 265000000 475
1980 57000000 418
1979 80000 360
Table 4.1 GDP per capita in year 2006 (yuan)
`City GDP population GDP per capita(yuan)
Beijing 7870.28 15810000 49780.39216
Tianjin 4359.15 10750000 40550.23256
Hebei 11660.43 68980000 16904.07364
Shanxi 4752.54 33750000 14081.6
Inner Mongolia 4791.48 23970000 19989.48686
Liaoning 9251.15 42710000 21660.38399
Jilin 4275.12 27230000 15700.03672
Heilongjiang 6188.90 38230000 16188.59534
Shanghai 10366.37 18150000 57114.98623
Jiangsu 21645.08 75500000 28668.98013
Zhejiang 15742.51 49800000 31611.46586
Anhui 6148.73 61100000 10063.38789
Fujian 7614.55 35580000 21401.20854
Jiangxi 4670.53 43391287 10763.7508
Shandong 22077.36 93090000 23716.14567
Henan 12495.97 93920000 13304.90843
Hubei 7581.32 56930000 13316.91551
Hunan 7568.89 63420000 11934.54746
Guangdong 26204.47 93040000 28164.7356
Guangxi 4828.51 47190000 10232.06188
Hainan 1052.85 8360000 12593.89952
Chongqing 3491.57 28080000 12434.3661
Sichuan 8637.81 81690000 10573.88909
Guizhou 2282.00 37571800 6073.704214
Yunnan 4006.72 44830000 8937.586438
Tibet 291.01 2810000 10356.22776
Shaanxi 4523.74 37350000 12111.75368
Gansu 2276.70 26060000 8736.37759
Qinghai 641.58 5480000 11707.66423
Ningxia 710.76 6040000 11767.54967
Xinjiang 3045.26 20500000 14854.92683
Table 4.2
Ensured Reserves of Major Energy and Ferrous Metals by Region (2006)
Source: China statistic yearbook
Region Petroleum (10 000 tons) Natural Gas (100 million cum) Coal (100 million tons)
Beijing 3074.99 275.75 2.97
Tianjin 16338.63 240.59 68.15
Hebei 0.00 0.00 1051.66
Shanxi 5526.32 1643.04 802.33
Inner Mongolia 17010.38 202.91 49.75
Liaoning 16529.56 167.84 17.11
Jilin 62196.71 935.83 77.67
Heilongjiang 0.00 0.00 0.00
Shanghai 2503.77 22.71 18.30
Jiangsu 0.00 0.00 0.49
Zhejiang 137.88 0.02 118.74
Anhui 0.00 0.00 4.79
Fujian 0.00 0.00 8.18
Jiangxi 34747.87 348.36 103.25
Shandong 5370.67 110.42 123.30
Henan 1187.18 3.85 3.26
Hubei 0.00 0.00 20.12
Hunan 9.00 0.31 1.89
Guangdong 175.16 3.48 8.46
Guangxi 40.80 8.90 0.90
Hainan 0.00 1135.76 18.26
Chongqing 345.05 5462.78 50.26
Sichuan 0.00 4.61 148.26
Guizhou 12.40 2.86 73.57
Yunnan 0.00 0.00 0.12
Tibet 19884.83 8587.65 277.57
Shaanxi 8727.59 98.91 61.70
Gansu 4377.23 1496.10 20.66
Qinghai 139.91 1.67 70.06
Ningxia 41883.22 6598.24 127.28
Xinjiang 0.00 0.00 5.73
Ocean 3074.99 275.75 2.97
Table 4.3 Land Use by Region (2006)
Source: China statistic yearbook
(10000 Hectare)
Region Area under Land Survey
Land for
Construction Percent
Land for Transport
Facilities
Percent
National National National
National Total Total Total Total 95069.3 95069.3 95069.3 95069.3 3272.0 3272.0 3272.0 3272.0 100% 100% 100% 100% 244.4 244.4 244.4 244.4 100% 100% 100% 100%
Beijing 164.1 33.3 20.27% 3.1 1.90%
Tianjin 119.2 36.0 30.23% 2.1 1.73%
Hebei 1884.3 178.2 9.46% 11.9 0.63%
Shanxi 1567.1 86.5 5.52% 6.2 0.40%
Inner Mongolia 11451.2 147.8 1.29% 15.8 0.14%
Liaoning 1480.6 139.1 9.40% 9.1 0.61%
Jilin 1911.2 106.0 5.55% 6.6 0.35%
Heilongjiang 4526.5 148.3 3.28% 11.8 0.26%
Shanghai 82.4 24.3 29.48% 1.9 2.34%
Jiangsu 1067.4 190.2 17.82% 12.7 1.19%
Zhejiang 1054.0 101.3 9.61% 9.0 0.86%
Anhui 1401.3 165.2 11.79% 9.9 0.71%
Fujian 1240.2 63.1 5.09% 7.6 0.61%
Jiangxi 1668.9 94.0 5.63% 7.2 0.43%
Shandong 1571.3 248.9 15.84% 16.2 1.03%
Henan 1655.4 217.8 13.15% 12.1 0.73%
Hubei 1858.9 139.0 7.48% 9.0 0.48%
Hunan 2118.6 137.4 6.48% 9.7 0.46%
Guangdong 1798.1 177.7 9.88% 12.0 0.67%
Guangxi 2375.6 94.4 3.97% 8.7 0.37%
Hainan 353.5 29.6 8.37% 1.4 0.39%
Chongqing 822.7 58.6 7.12% 4.8 0.58%
Sichuan 4840.6 158.8 3.28% 13.4 0.28%
Guizhou 1761.5 55.2 3.13% 5.9 0.34%
Yunnan 3831.9 79.9 2.08% 9.8 0.26%
Tibet 12020.7 6.6 0.05% 2.4 0.02%
Shaanxi 2058.0 80.9 3.93% 6.5 0.32%
Gansu 4040.9 97.2 2.41% 6.5 0.16%
Qinghai 7174.8 32.5 0.45% 3.1 0.04%
Ningxia 519.5 20.9 4.02% 1.8 0.35%
Xinjiang 16649.0 123.4 0.74% 6.2 0.04%
Table 4.4 Population by Urban and Rural Residence and Region (2006)
Source: China statistic yearbook
(10 000 persons)
Region
Total Population Urban Population Rural Population
(year-end) Population Proportion Population Proportion
National National National
National Total Total Total Total 131448 131448 131448 131448 57706 57706 57706 57706 43.90 43.90 43.90 43.90 73742 73742 73742 73742 56.10 56.10 56.10 56.10
Beijing 1581 1333 84.33 248 15.67
Tianjin 1075 814 75.73 261 24.27
Hebei 6898 2652 38.44 4246 61.56
Shanxi 3375 1452 43.01 1923 56.99
Inner Mongolia 2397 1166 48.64 1231 51.36
Liaoning 4271 2519 58.99 1752 41.01
Jilin 2723 1442 52.97 1281 47.03
Heilongjiang 3823 2045 53.50 1778 46.50
Shanghai 1815 1610 88.70 205 11.30
Jiangsu 7550 3918 51.90 3632 48.10
Zhejiang 4980 2814 56.50 2166 43.50
Anhui 6110 2267 37.10 3843 62.90
Fujian 3558 1708 48.00 1850 52.00
Jiangxi 4339 1678 38.68 2661 61.32
Shandong 9309 4291 46.10 5018 53.90
Henan 9392 3050 32.47 6342 67.53
Hubei 5693 2494 43.80 3199 56.20
Hunan 6342 2455 38.71 3887 61.29
Guangdong 9304 5862 63.00 3442 37.00
Guangxi 4719 1635 34.64 3084 65.36
Hainan 836 385 46.10 451 53.90
Chongqing 2808 1311 46.70 1497 53.30
Sichuan 8169 2802 34.30 5367 65.70
Guizhou 3757 1032 27.46 2725 72.54
Yunnan 4483 1367 30.50 3116 69.50
Tibet 281 79 28.21 202 71.79
Shaanxi 3735 1461 39.12 2274 60.88
Gansu 2606 810 31.09 1796 68.91
Qinghai 548 215 39.26 333 60.74
Ningxia 604 260 43.00 344 57.00
Xinjiang 2050 778 37.94 1272 62.06
Table 4.5 Number of Students per 100 000 Population by Level (2006)
Source: China statistic yearbook
Year Senior
Secondary
Higher Education Region
2004 2792 1420
2005 3070 1613
2006 3321 1816
Beijing 3488 34.88% 6897 68.97%
Tianjin 3848 38.48% 4600 46.00%
Hebei 3618 36.18% 1630 16.30%
Shanxi 3851 38.51% 1790 17.90%
Inner Mongolia 3379 33.79% 1413 14.13%
Liaoning 3172 31.72% 2379 23.79%
Jilin 2906 29.06% 2359 23.59%
Heilongjiang 2549 25.49% 2090 20.90%
Shanghai 2782 27.82% 4206 42.06%
Jiangsu 4058 40.58% 2301 23.01%
Zhejiang 3539 35.39% 2115 21.15%
Anhui 3520 35.20% 1351 13.51%
Fujian 3826 38.26% 1656 16.56%
Jiangxi 3612 36.12% 2105 21.05%
Shandong 3711 37.11% 1811 18.11%
Henan 3652 36.52% 1331 13.31%
Hubei 4079 40.79% 2542 25.42%
Hunan 3594 35.94% 1719 17.19%
Guangdong 3089 30.89% 1591 15.91%
Guangxi 2771 27.71% 1228 12.28%
Hainan 2649 26.49% 1374 13.74%
Chongqing 3389 33.89% 1906 19.06%
Sichuan 3057 30.57% 1414 14.14%
Guizhou 2261 22.61% 910 9.10%
Yunnan 2059 20.59% 1042 10.42%
Tibet 1894 18.94% 1014 10.14%
Shaanxi 4285 42.85% 2549 25.49%
Gansu 3334 33.34% 1427 14.27%
Qinghai 2752 27.52% 935 9.35%
Ningxia 3406 34.06% 1511 15.11%
Xinjiang 2906 29.06% 1416 14.16%
Table 5.1
Correlation between the independent variables and the dependent variable
Correlations Correlations Correlations Correlations
