2. Practical Part
2.1 Analyze the impact of economic factors related to family planning on the
2.1.3 Practical results and analysis
2 . 1.3.1 Descriptive statistical analysis results
Variable Sample Size Minimum Maximum Average Std.Error
GDP 21 825.58 9409.57 4153.875 3059.463
Family Size 21 3.05 3.65 3.219 0.177
Savings 21 395.11 6013.47 2678.43 2048.21
CPI 21 98.6 105.9 101.881 2.001
Education 21 89.373 412.387 278.474 112.975
Healthcare 21 305.4 3921.3 1521.109 1205.863
Table 2-1. Describe statistical analysis results Source: Own calculations
From the minimum to the maximum of GDP, the gap is very large, almost from 1000 to 10,000. It can be seen that China's economic development has been so fast in the past 20 years. There have also been significant changes in the average size of the family. From 3.85 persons in 1998 to 3.05 in 2018. If we compared with the US, as of 2018, the U.S.
Census Bureau counted about 83.09 million families in the United States. The average family consists of 3.14 persons in 2018. China has achieved a lower family size than the United States, which is inseparable from the family planning policy. The level of the savings rate determines the speed of economic development. China’s national savings rate has nearly turned 15 times in 20 years. This also proves the rapid development of China's economy. China's household consumption index is relatively stable, and CPI is an important indicator for observing inflation. We can see that China is basically stable at 2% inflation. Education plays an important role in promoting the optimization of human capital for economic development. This role is the main factor for economic growth. It is a kind of capital with economic value. The growth of per capita medical expenses in China is significantly lower than the rate of economic growth. However, the per capita medical expenses have also increased by 13 times. This is due to the health
care system and the pension system. Descriptive statistics of the remaining variables and other related test values can be found in picture 2-1, and will not be repeated here.
2.1.3.2 Correlation analysis
Before the multivariate linear regression of the model, the Pearson correlation coefficient is used to analyze the correlation coefficient between the variables to ensure that there is no serious multi-collinearity problem in the multivariate regression. The results are shown in Table 2-1.
Pearson related coefficient
GDP($) Average Family Size Pearson Coefficient -0.761***
P-value 0.000
Gross Savings Pearson Coefficient 0.998***
P-value 0.000
CPI(last year =100 ) Pearson Coefficient 0.292
P-value 0.000
Education Level Pearson Coefficient 0.927***
P-value 0.000
Healthcare Level Pearson Coefficient 0.984***
P-value 0.000
Table 2-2. Pearson related coefficient
Note: ***, **, and * indicate significant at 1%, 5%, and 10%, respectively.
From the above table, we can use correlation analysis to study the correlation between GDP ($) and average family size, total savings, CPI (last year = 100), education level, and health care level, using Pearson correlation coefficient. A detailed analysis of the strengths and weaknesses of the correlation can be seen:
The Pearson correlation coefficient between GDP ($) and the average family size is -0.761, and exhibits a significant level of 0.01, thus indicating a significant negative correlation between GDP ($) and average household size. The correlation coefficient between GDP ($) and Gross Savings is 0.998 and exhibits a significant level of 0.01, thus indicating a significant positive correlation between GDP ($) and Gross Savings.
The correlation coefficient between GDP ($) and CPI (last year=100) is 0.292, close to 0, and the P value is 0.199>0.1, thus indicating there is no correlation between GDP ($) and CPI (last year=100). The correlation coefficient between GDP($) and Education Level is 0.927, and it shows a significant level of 0.01, which indicates a significant positive correlation between GDP ($) and Education Level. And the same for GDP($) and Healthcare Level, the correlation coefficient is 0.984, and it shows a significant level of 0.01, thus indicating a significant positive correlation between GDP ($) and Healthcare Level. Among them, the core variable of this thesis is the average family size.
2.1.3.3 Regression analysis
Results of Regression Analysis Unstandardized
Coefficients
Standardized Coefficients
T-value P-value R²
B Std. Beta 0.999
Constant 3,849,722 1,789,704 - 2,151 0.0048***
Family Size
-772,415 368,679 -45 -2,095 0.054*
Savings 1,374 69 0.92 19,929 0.000***
CPI -5,627 11,652 -4 -483 0.636
Education -3,508 1,177 -0.13 -2,981 0.009***
Healthcare 435 75 171 5,789 0.000***
Dependent Variable: GDP($) D-W: 2.517
Table 2-3. Regression Analysis Results
Note: ***, **, and * indicate significant at 1%, 5%, and 10%, respectively.
Source: Own calculations
As can be seen from the above table, the model’s R-squared value is 0.999, meaning that the average family size, gross savings, CPI, education, and healthcare levels can explain the 99% change in GDP. When the model was tested by F, it was found that the model passed the F test (F=6208.306, p<0.05), which means that the five independent variables, such as the average family size and the total savings rate, have at least one indicator influence on GDP. The model can be explained by：
GDP= 3849.722 - 772.415 * average family size + 1.374 * gross savings - 5.627 * CPI - 3.508 * educational level + 0.435 * healthcare level.
(2)
Picture 2-4. Scatter plot of family size (vertical) and GDP (horizontal) Source: Own calculations
The regression coefficient of our core variable (average family size) is -772.415 ( p=0.054＜0.1)，which means family size will have a negative impact on GDP at a significant level of 0.1. If we reduce one person in our family size, then the GDP per capita will increase by $772.415. This is in line with our assumption that China's excessive population does impose burdens and pressure on China's economy. From the picture above, the horizontal represents the GDP expressed in US dollars in the past 20
years, and the vertical represents the average family size. The size of the family has a significant downward trend with the increase of GDP. And China’s family planning policy has reduced the size of the family but contributed to economic growth.
The phenomenon of low fertility is not unique to China. At the end of the 20th century, there was a general trend of lower fertility rates worldwide. Women also have the dual status of material producers and social reproducers—that is, they must engage in material production/paid labor, but also engage in social reproduction such as housework, parenting, and care. The dual identity of women makes economic development, gender equality and fertility closely related. The reason why China's relatively low fertility rate is mainly due to the following points: 1. China's unique family planning policy: mandatory restrictions on fertility. Although the policy of opening the second child has been opened since January 1, 2016, China’s fertility rate has shown a new low level. 2. China's education level is constantly improving: Since 1999, Chinese universities have been expanding their enrollment, and more and more people have undergraduate degrees. Why does the expansion of the university lead to a decline in fertility? Because of the expansion of college enrollment, women’s marriage age has increased and women’s employment rate has risen. Moreover, data from many countries in the world show that the increase in female education will lead to a decline in fertility. 3. China’s price level is still high, the national average house price level has risen 2.5 times in the past ten years, and the coastal cities in the southeast have risen more than five times, far exceeding the affordability of ordinary people. The price to income ratio has reached almost 26.08 in Peking and the cost of living index has reached 51.42. Many young people can't afford a house, they can't get married, and of course, they can't afford to have children. Even if I buy a house by a mortgage loan, because of the mortgage, many young couples become "house slaves" and dare not have children.
The regression coefficient of gross savings is 1.374 ( p<0.01), which has a significant positive impact on GDP. When the per capita resident savings increase by $ 1, GDP increases by $1.374. If the savings rate is too high or too low, it will both hurt economic benefits. Especially in the US, the savings rate is very low, but the consumption is very high. If the people exceed their own borrowing capacity and there is no corresponding source of funds, certain credit expansion will be formed. The emergence of the US
not conducive to full employment, and a large amount of production capacity is idle, which is not conducive to the optimization of industrial structure. At the same time, high savings also reflect inadequate and unpredictable social security and high taxes.
However, only from the regression analysis results of our data, we can get China's savings rate affecting the growth rate of GDP, although the growth rate is not large, it still has a positive impact.
The regression coefficient of CPI is -5.627 ( p=0.636>0.1), which means that CPI does not affect GDP.
The level of education and health can be discussed together and both belong to the Human Development Index. According to the regression results, both the education level and the medical level have an impact on GDP at a significant level of 0.01. The medical level has a positive impact on GDP. If the annual per capita medical expenditure increases by $1, the GDP will increase by $0.435. Conversely, ceteris paribus, if the number of people with an undergraduate degree increases by one million, the GDP will decrease by $3.508.
Picture 2-5. Scatter plot of GDP and Healthcare level Source: Own calculations
As can be seen from the picture, annual healthcare per capita expenditure has a very stable slope before per capita GDP increases to $7,500. When GDP is between $7,500 and $10,000, healthcare spending is slowly declining. As stated in the Finance and Development, Bill of Health ( Benedict Clements and St. Gif Gupta, 2014), controlling
public health spending growth is one of the most important financial issues. Over the past 30 years, such expenditure has grown substantially (Clements, Coady and Gupta, 2012), accounting for almost half of the increase in non-interest government spending during this period. Public health spending growth in developed economies has been slow in the short term, but this is unlikely to continue.
The growth in public health spending has slowed, and this phenomenon occurs almost simultaneously in all developed economies. However, whether the slowdown in growth will continue depends on future trends in the potential drivers of spending. There are five main factors. 1. Aging population: In general, the demand for health care will increase with age. In the next 20 years, as the life expectancy continues to increase, the average age of the population in developed economies will increase, which may lead to further increases in healthcare spending. 2 Revenue growth: Revenue growth usually leads to more and better incomes that require health care services. However, the exact value of income elasticity (changes in demand for health care due to changes in income) is a hot topic of discussion and there is no clear answer. Recent studies have shown that income elasticity of demand for healthcare services is below or close to 1.0 (Maisonneuv Ageingtins, 2013). 3. Technological advances: Advances in medical technology are the most important determinants of health care spending. The continuous development of new surgical methods and medicine has greatly expanded the prevention and treatment of various diseases, but the technology is expensive. Surgery costs have also led to a rapid increase in health care spending. 4. Baum effect: The Baum effect, named after its founder and economist William Bowmore, refers to the relatively high growth of labor costs in industry units where productivity levels are not easily improved; including government-provided services. In manufacturing, productivity can be increased by implementing new processes to reduce the number of workers needed to produce a certain output. However, in the field of health care, the possibility of reducing medical personnel without lowering the level of service is very limited, so it is difficult to increase labor productivity. The wages of the health care industry will rise with the average of the economic system, but productivity will not.
Therefore, the increase in labor costs per unit of health care is more pronounced. 5.
Health care policies and systems: Health care policies and systems can be used to influence supply and demand to influence spending levels. On the demand side, the policy determines the coverage of the public benefit plan or the proportion of patient
side, policies directly affect spending (for example, public clinic spending) or indirectly to private hospitals and doctors funded by public health insurance.
Picture 2-6 Scatter plot of GDP and Education level Source: Own calculations
According to historical analysis, the enrollment rate of primary schools in China is relatively high. However, the enrollment rate of higher education in secondary schools and universities is significantly lower than the world average level. China's enrollment rate of universities is only rank 87 in the world (2012). According to the World Bank's international economic accounting data, China's GDP reached 8.23 trillion US dollars in 2012, and the total public education expenditure reached 335.8 billion US dollars. It accounts for 4.08% of GDP. For the first time in history, China’s education spending accounted for more than 4% of GDP. The National Education Public Expenditure, which accounts for 4% of GDP, is the basis for measuring the level of education in the world. When the public education expenditure reaches 4.06%-4.25% of GDP, the benign development of education and economy can be realized.
The graph above can be seen that the number of undergraduate students in China is growing but is not at a fast speed. The correlation between GDP and education level is even negative. China still needs great improvement in investment in education. Only when education and the economy develop in harmony, then it can achieve a win-win result.