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Paper代写:China's economic development

2018-12-06 | 来源:51due教员组 | 类别:Paper代写范文

下面为大家整理一篇优秀的paper代写范文- China's economic development,供大家参考学习,这篇论文讨论了中国的经济发展问题。从中国的社会财富的角度来看,存在解体的趋势。在过去的30年里,中国的基尼系数一直在以惊人的速度上升,中国城乡的差距越来越大,城乡之间缺乏基础设施和教育资源。对于世界上所有快速发展的经济体来说,这是一个正在出现的问题,而且这种差距已经成为中国人口从欠发达地区向发达地区迁移的动力。

economic development,中国经济发展问题,论文代写,essay代写,paper代写

1. Introduction

There has been a trend of disintegration in terms of social wealth (Yoshida, 2012). In the past 30 years, the Gini coefficient of China has been rising at alarming speeds. There exists an increasing gap between Chinese cities and rural areas, with the latter lacking on basic infrastructure and educational resources. This has been an emerging problem for all fast-developing economies in the world. This gap has become the motivation of population migrations in China, from less developed areas into more developed areas in general (Lary, 2012). The major cities in China, represented by Shanghai, Beijing, Guangzhou and Shenzhen, has taken in millions of working forces within a decade. In the first part of the data analysis, the change before and after the migration is shown on a five-year scale. While the major cities are attracting population like magnets, the population in rest of areas in China is believed to be largely stable. This hypothesis will be tested later.

A quantification of the gap in social wealth is then presented in Lorenz curves. The Gini coefficients are calculated based on them to demonstrate an increase. Finally, as GDP is an indicator of not only the economic status and development of a region, but also an effective indicator of the social development of the area in general. In the last part of the analysis, this theory will be tested with the Pearson Correlation established between GDP and education and urbanization parameters. With the data of China’s population by region, education, GDP etc., the goal of this report is to find the relocation of population, the distribution of social wealth, as well as the correlation between GDP and two parameters of development, urbanization and tertiary education. The focus period of this report is from 2000 to 2005, which is the first five years of the new century and a transitional period for China. This report will provide a new perspective based on the existing research by focusing on the period from 2000 to 2005. It will examine the correlation between GDP and education and urbanization as well as the change of such a correlation over the years, which has not been covered in existing literature. Through this report, a more thorough understanding of the relationship between GDP and the societal development will be established. This is combined with the migration of population and the emerging social class differences, so that a complete picture of the societal conditions will be depicted.

2. Research Design and Data

The research is divided into three major subtopics as mentioned above. Firstly, a most direct representation of the movement of population in China will be provided, since it serves as an entrance for the reader to understand the background and motivate them to find more about the reasons behind the mass migrations. A map of China is used with different population sizes represented by different colors in it. With the lightest color representing the least populated area, and the darkest representing the most populated area, the maps provide the most intuitive way from the start of the report. The maps of 2000 and 2005 will be presented and compared, so that findings can be spotted right from them.

The second part of the analysis deals with the messier part of the data, as it seeks to find the change in Gini Coefficient over the years. Due to the lack of access to more accurate data, this report adopts a more qualitative approach instead of providing precise values of Gini Coefficients. Therefore, it would not be surprising to find discrepancies in the value of the Gini coefficients calculated in this report and the ones from outside sources. However, this should not hinder the reader from obtaining the general trend in the change of numbers. In this report, the Gini coefficients are calculated based on unit of provinces. Therefore, personal income is substituted with the GDP of provinces for convenience, while the number of people is replaced with the population of entire provinces (cities). Although some may argue about the lack of precision in such substitutions, this approach provides a “zoomed-out” view of the social class gaps. Instead of looking at the gaps in personal income, the Gini coefficients presented in this report demonstrates the gap between different provinces, which is also a crucial aspect that should not be overlooked

Lorenz curves of the year 2000 and 2005 are drawn respectively, from the data points of cumulative provincial population and their GDP. Their order has been predetermined, from calculating the GDP for an average person in the province through division. Based on the result of the average GDP of different provinces, the original values of population and GDP are ranked in ascending order before being converted into a scale of 100. These values were then drawn into cumulative Lorenz curves. The two curves were then fitted into polynomial equations to calculate the area under the curves, which indirectly leads to the value of S. The Gini coefficients in this report are obtained by dividing this number with P, which is the area of the triangle.

Finally, the correlation between GDP and education, GDP and urbanization is established through Pearson Correlation. This method is one of the most straight-forward ways in finding the relationship between two sets of values. For the urbanization part, the percentage of urban population is used as an indicator of urbanization, which is considered a fair assumption. The function “CORREL ()” in excel is the used to derive the value of r from the two lists of numbers to find a correlation. The values of r of year 2000 and 2005 will also be compared.

For the education part, a simplification is made so that something meaningful can be derived from the messiness of the data, by representing the development of education with the number of people with a college education and above. Although there is certain inaccuracy in this representation, the population who have benefited from tertiary education are believed to be one of the simplest indicator of the development of the local education system. the same process is then executed.

3. Analysis and major findings

As observed from figure one, there has already been early trends of polarization of population in 2000, with Sichuan, Guangdong, Shandong and Henan being the four distinctive areas with the largest populations. The provinces surround these four, especially in the east of China, is also darker than other areas, which shows a polarization effective of population in general. The most significant change observed in 2005 is the reduction of population in Sichuan. This is largely due to the emigration of its population to the eastern and more developed areas. While Henan, Shandon, Anhui and Guizhou are connecting themselves with darker colors, it is interesting that the area around Fujian remains lighter during the five years. However, with the other provinces closing in on it, it is predicted that Fujian will experience a rise in population in the following years.

Figure 1. Population of China by Province, 2000.

Figure 2. Population of China by Province, 2005.

Figure 3 shows the comparison between two Lorenz Curves of years 2000 and 2005. From this curve, the difference in the S areas seems rather minimal. In the more detailed calculations of the two curves respectively, the fitted equation is shown in the graphs, which is the integrated to find the area under the curves. The S areas obtained from the two Lorenz curves are 1178.5 and 1421.’ For 2000 and 2005 respectively. This leads to Gini coefficient in 2000 to be 0.236, and the value of 2005 to be 0.284. Although these values may seem lower than existing results, they have shown a 20% increase within five years, which explain the migration trends observed from part one. It is the enlarging gap in social wealth that drive more and more population from poorer areas to more developed one. The developed areas thus have better manpower and access to resources to maintain a higher speed of development, which would further widen the social gaps. This result is in accordance with the existing literature.

Figure 3. Comparative Lorenz Curves

Figure 4. Lorenz Curve of 2000.

Figure 5. Lorenz Curve of 2005.

In the final part of the data analysis, the correlation between urbanization and GDP is established. The value of r for the year of 2000 is calculated to be 0.283, while the year 2005 yields the result of 0.318. Surprisingly, these values are much smaller than expectation. The level of urbanization and GDP are only found to be weakly correlated with each other. This has shown that urbanization on the provincial scale is less likely to have a significant influence on GDP, due to the involvement of other factors. Moreover, an increase from 0.283 to 0.318 is observed, which shows that cities are playing an increasingly important role in the development of economy. This correlation is expected to increase further in the following years, which can be verified if needed.

Some interesting results have been generated in the establishment of correlation between tertiary education and GDP. While the value of r is only 0.070 for 2000, it jumps to 0.900 in 2005. A value of 0.070 means very weak correlation to the level of being neglected. In contrast, 0.900 is a very strong positive linear correlation which indicate that GDP and the number of college graduates are very closely related. After multiple checks in the values used, the source of such a jump has yet to be found. While there is no doubt that GDP and tertiary education is somehow correlated, the jump is simply too big to be of natural causes. One potential explanation could be the source of data. As explained in the data sheet, the numbers for 2000 are obtained from the advanced tabulation of the 5th national population census, with zero hour of November 1, 2000 as the reference time. In compassion, the data of 2005 were obtained from sampling. This difference in data collection method can be the reason for the discrepancy.

4. Conclusion and discussion

In conclusion, this report has successfully made some sense out of the large amount of original data. Organization of data, as have been observed in the process of analysis, is only the most effective with a clear goal of what to find in mind. Without a defined purpose of study, it is easy for one to get lost in the numbers without knowing what to do. From the first part of the study, it has been found that there have been early patterns of population migration from the west to the east of China. While the areas that are distant from the provinces with the largest populations remain relatively stable, with Sichuan even on the decline, the areas near these provinces are experiencing increases as well, which is clearly the influence of the population polarization. The motivation for such polarization is explained with the 20% increase in Gini coefficient in five years. This gap is predicted to be widened further without government intervention. Finally, a weak but growing correlation between GDP and urbanization is obtained. Improvements in the use of data may locate a stronger correlation, as the percentage of urban population may not be an accurate indicator of level of urbanization. There remains the problem with education-GDP correlation, with the source of error unidentified. However, the 0.900 value of r in 200’ indicates that GDP and tertiary education go hand in hand.

Building on the experience of this report, it is possible to expand the coverage of data to the current year, so that a bigger picture of the socio-economic development of China can be obtained. In general, the findings from the data has agreed with existing literature, while some of the expectations were not met. People are able to see more by looking as the change in correlation, especially when it is related with GDP of a region. By comparing the strength of correlations between different parameters and the GDP value, one can understand the inclination of national policy, as well as make predictions of the near future based on the existing trend.

5. Literature Cited

Yoshida, J. (2012). China: Gap between rich, poor grows wider. Electronic Engineering Times, (1627), 46.

Lary, D. (2012). Chinese migrations: The movement of people, goods, and ideas over four millennia. Lanham: Rowman & Littlefield Publishers, Inc.

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