Linear Regression Analysis of GDP Growth and Urbanization Rate in Mainland of China ()
1. Introduction
Urbanization/urbanisation, also known as urbanization, refers to the transformation of a country or region from a traditional rural society dominated by agriculture to a rural society with the development of social productivity, advancement of science and technology and adjustment of industrial structure. The historical process of the gradual transformation of modern urban society dominated by non-agricultural industries such as industry (secondary industry) and service industry (tertiary industry) [1] .
Real GDP per capita is an effective tool for people to understand and grasp the macroeconomic operating conditions of a country or region, that is, “GDP per capita”. It is often used as an indicator to measure economic development in development economics and is the most common indicator. One of the important macroeconomic indicators [2] . Comparing the GDP achieved by a country during the accounting period (usually one year) with the country’s permanent population (or registered population), the GDP per capita is obtained, which is a standard for measuring the living standards of people in various countries, often combined with purchasing power parity for a more objective measurement.
This article selects mainland of China as the research object. According to data released by the National Bureau of Statistics on May 11, 2021, the results of the seventh census: the population living in cities and towns is 901,991,162, accounting for 63.89%. It is generally believed that when the urbanization rate of a country is between 30% and 70%, it is in the stage of rapid development [3] . That is to say, urbanization in mainland of China is bidding farewell to the period of rapid growth and entering a new stage of high-quality development. The connotation of high-quality development of new urbanization can be summarized as the organic unity of high-quality urban construction, high-quality infrastructure, high-quality public services, high-quality living environment, high-quality urban management and high-quality citizenization [4] . Economically, mainland of China has bid farewell to the era of rapid economic growth and has entered a critical period of medium-to-high-speed economic development and high-quality development. Analyzing the relationship between economic growth and urbanization rate is of great significance to the development of new urbanization in the future high-quality development stage.
This article combines qualitative and quantitative analysis methods, which can not only bring into play the comprehensive, detailed and in-depth advantages of qualitative analysis methods, but also bring into play the systematic, objective, standardized and scientific advantages of quantitative analysis and prediction methods. On the basis of the construction of the indicator system, the latest national statistics are verified.
2. Research Methods and Data Sources
2.1. Research Methods
Correlation analysis is used to describe whether there is a relationship between variables, while regression analysis studies the influence of the relationship, reflecting the degree of influence of one X or multiple X on Y. However, correlation analysis can only study the direction and degree of correlation between variables, but it cannot obtain the specific form of the relationship between variables, nor can it infer the changes in another variable from the changes in one variable, which must be done through regression analysis. Therefore, this study first conducts relevant analysis on the per capita GDP data and urbanization rate data of each province in the country, and then determines whether regression analysis is needed.
Regression analysis involving only two variables is called single regression analysis [5] . The main task of single regression is to estimate one of the two related variables from the other variable. The estimated variable is called the dependent variable and can be set to Y; the estimated variable is called the independent variable and is set to X. Regression analysis is to find a mathematical model Y = f(X), so that Y can be estimated from X using a functional formula. When the form of Y = f(X) is a straight line equation, it is called linear regression. This equation can generally be expressed as Y = A + BX. According to the least squares method or other methods, the values of the constant term A and the regression coefficient B can be determined from the sample data. After A and B are determined, with an observed value of X, an estimated value of Y can be obtained. Whether the regression equation is reliable and how big the estimated error is should be tested for significance and error calculation. Whether there is a significant correlation and the size of the sample, etc., are factors that affect the reliability of the regression equation.
2.2. Data Sources
This article selects the per capita GDP data and urbanization rate data of various provinces in mainland of China in 2020, establishes a linear regression model, and uses SPSS software for analysis. The data comes from the National Bureau of Statistics, as shown in Table 1.
3. Result Analysis
3.1. Correlation Analysis between Urbanization Rate and per Capita GDP
Correlation analysis is a statistical analysis method that studies the correlation between two variables or two or more random variables with the same status. This article uses the currently more commonly used Pearson correlation coefficient, which is between −1 and +1. The closer the absolute correlation coefficient is to 1, the more closely the data points fall on a straight line. If the variables are positively correlated, the stronger the correlation, the closer the correlation coefficient is to −1; the stronger the negative correlation, the closer the correlation coefficient is to −1. If the correlation coefficient is close to 0, it indicates that there is no significant correlation.
On this basis, we first explore the correlation between industrial structure and employment structure. This article uses SPSS to conduct correlation analysis on the data, and the results are shown in Table 2.
According to Table 2, the following conclusions can be drawn: the correlation coefficient is 0.806, and the significance level is less than 0.01. It shows that there is a very high degree of positive correlation between the two, and the linear
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Table 1. Per capita GDP data and urbanization rate data of each province in the country in 2020.
relationship is very stable. It shows that the higher the per capita GDP, the higher the urbanization rate, and economic development is the endogenous decisive
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Table 2. Correlation results between industrial structure and employment structure.
**Correlation is significant at the 0.01 level (two-tailed).
a. Predictor variable: (constant), GDP per capita/yuan; b. Dependent variable: urbanization rate.
a. Dependent variable: urbanization rate.
force of urbanization.
3.2. Regression Analysis of GDP per Capita and Urbanization Rate
The above analysis shows that per capita GDP and urbanization rate are significantly related. In order to explain the relationship between the two in a deeper level, SPSS software is used to conduct linear regression analysis on the two.
Establish a model: Assume that the dependent variable Y is the urbanization rate and the independent variable X is GDP per capita, then the linear regression model is:
Among them, (β0 + β1X) the influence of the explanatory variable x on the explained variable y, that is, the influence of the independent variable on the dependent variable, and ε represents the influence of uncertain random factors on the dependent variable y. In order to further study whether the assumptions of the established regression model are established, the residuals were used to create the data shown in the figure below (See Table 3).
Judging from the goodness of fit of the data shown in the figure above, R2 is close to 1, which indicates that the prediction accuracy of the equation is high.
As can be seen from the above figure, the regression coefficients are −64440.632 and 2259.604 respectively. Therefore, the linear regression equation of GDP per capita, the linear relationship between the two is very significant. In addition, β is 0.987, indicating that the more investment, the higher the GDP (See Table 4 and Table 5, Figure 1).
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Table 5. Residual statistics table.
a. Dependent variable: urbanization rate.
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Figure 1. Normal PP plot of regression standardized residuals.
4. Conclusion
This article analyzes their per capita GDP data and urbanization rate in mainland of China and finds that there is a strong correlation between the two. It can be seen that the growth of per capita GDP will promote the urbanization process. This is in line with the actual situation in mainland of China and the relevant experience of developed countries in recent decades, and also verifies the theory that “economic development is the endogenous decisive force of urbanization” [1] . However, there is no upper limit to economic growth, but there is an upper limit to the urbanization rate. When urbanization reaches a certain level, improving the quality of urbanization is a new goal of urban development, and the concept of new urbanization came into being. In short, on the new track of new urbanization, economic development is still the top priority.