Frontiers in Business, Economics and Management 
ISSN: 2766-824X | Vol. 15, No. 2, 2024 

 

253 

Comprehensive Analysis of the Socio-Economic 
Situation of Zhejiang Province 

-- Based on the disposable income of residents in Zhejiang Province 

Yida Ren * 

Henan University, Accounting, China 
* Corresponding author: Yida Ren (Email: 1293098511@qq.com) 

 

Abstract: Socialism with Chinese characteristics has entered a new era, and achieving high-quality economic development is 
an important task for building a modern socialist country in an all-round way. The report of the 20th National Congress of the 

Communist Party of China pointed out that consumption and disposable income play a key role in economic development. This 

paper uses two statistical analysis methods, descriptive statistics and linear regression analysis, and analyzes the socio-economic 

and economic situation of Zhejiang Province in China according to the economic and social development index of residents' 

disposable income, and analyzes the problems of economic and social development faced by Zhejiang Province and discusses 

solutions, so as to promote the comprehensive, sustainable and healthy social and economic development of Zhejiang Province. 

Keywords: Disposable income of residents in Zhejiang Province, Socio-economic analysis, Descriptive statistics, Linear 
regression analysis. 

 

1. Introduction 

1.1. Background and Significance  

First of all, Zhejiang Province has a special geographical 

location, located on the southeast coast of China and the 

southern wing of the Yangtze River Delta, with strong overall 

economic and financial strength, which is at the leading level 

in China, with a total GDP of 7,771.5 billion yuan in 2022. 

Therefore, the analysis of the economic development of 

Zhejiang Province is an indispensable link, and it can be used 

as a reference for the development of various regions in 

Zhejiang Province.  

Second, Zhejiang Province is currently in a climax period 

of economic construction and development. The province is 

undergoing economic transformation and developing in the 

direction of "building a modern economic system". Therefore, 

Zhejiang Province has established seven major industrial 

transformations: information, environmental protection, 

health, tourism, fashion, finance, and high-end equipment 

manufacturing. How Zhejiang Province can seize this 

development opportunity is a particularly important issue.  

Finally, the impact of the epidemic has brought different 

degrees of impact and impact on the social and economic 

operation of prefecture-level cities in Zhejiang Province. In 

terms of economy and finance, the GDP of Lishui City and 

Huzhou City was relatively affected by the epidemic, and 

Taizhou City's fiscal revenue declined more in 2020. Except 

for Zhoushan, the GDP growth rate of all prefecture-level 

cities in Zhejiang Province in 2020 declined to a certain extent 

compared with 2019.  

Therefore, in this context, this paper selects the disposable 

income of residents in Zhejiang Province, which is suitable 

for the economic development of Zhejiang Province, and uses 

two statistical methods, descriptive statistics and linear 

regression analysis, to select the index data related to the 

economic and social development of Zhejiang Province in 

recent years, and compare them with the actual daily 

observations, so as to accumulate experience for the future 

economic and social development of Zhejiang Province and 

give reasonable and feasible development suggestions. 

2. Comprehensive Evaluation and 
Analysis of Economic Development 
in Zhejiang Province  

2.1. Data Sources and Processing  

On the basis of synthesizing the existing research work, this 

paper preliminarily selects the following six factors from 

2000 to 2021 as the explanatory variables of disposable 

income (Y) of residents in Zhejiang Province by querying the 

statistical yearbooks of the National Bureau of Statistics and 

the statistical yearbooks of the Zhejiang Provincial Bureau of 

Statistics, as shown in Table 1.

 

 

 

 

 

 

 

 

 



 

254 

Table 1. Data display 

Year X1 X2 X3 X4 X5 X6 Y 

2000 10954.7 95 5027 100280.1 12581.51 39.8 9721 

2001 12205.4 103.6 5350 110863.1 15301.38 41.2 10070 

2002 13638.1 133.7 6089 121717.4 17636.45 42.2 14532 

2003 15329.6 187.7 6587 137422 20017.31 42 15007 

2004 17615 239.1 7280 161840.2 24165.68 41.2 15661 

2005 20627.1 306.8 8068 187318.9 28778.54 41.3 16385 

2006 24262.3 377.5 8851 219438.5 34804.35 41.8 17229 

2007 29471.5 447.8 10196 270092.3 45621.97 42.9 18584 

2008 35289.5 512.0 11489 319244.6 54223.79 42.9 20957 

2009 40288.2 531.1 12558 348517.7 59521.59 44.4 25977 

2010 47269.9 575.4 13821 412119.3 73210.79 44.2 27359 

2011 59954.7 608.2 15554 487940.2 89738.39 44.3 28971 

2012 70914.2 624.7 17107 538580 100614.3 45.5 29550 

2013 93064.3 638.7 18488 592963.2 110530.7 46.9 30851 

2014 102817.2 659.4 19968 643563.1 119175.3 48.3 32658 

2015 112007.8 680.9 21392 688858.2 124922.2 50.8 35537 

2016 120074.8 704.2 23079 746395.1 130360.7 52.4 38529 

2017 129889.1 735.8 24445 832035.9 144369.9 52.7 42046 

2018 141480 753.3 26112 919281.1 156402.9 53.3 45840 

2019 154296.1 758.5 28063 986515.2 158000.5 54.3 49899 

2020 164126.9 797.2 27007 1013567 154312.3 54.5 52397 

2021 180817.5 826.5 30307 1143670 172730.5 53.3 57541 

X1: The total wages of employed persons in urban units in 

Zhejiang Province (100 million yuan), which refers to the 

wage level of employees in a certain period of time, which 

can be used to reflect the social distribution.  

X2: The number of graduates of regular colleges and 

universities (10,000 people) reflects the development of 

social education and the composition of personnel. 

Educational attainment affects the income of residents, and 

usually those with higher education have higher income levels.  

X3: Per capita consumption expenditure of urban residents 

(1 yuan), the larger the per capita consumption expenditure of 

urban residents, the higher the consumption level, the more 

consumption, and the higher the disposable income of 

residents.  

X4: Gross domestic product of Zhejiang Province (100 

million yuan), which is equal to the sum of the added value of 

various industries, reflects the overall economic development 

level of Zhejiang Province.  

X5: Tax (100 million yuan), tax directly affects residents' 

disposable income.  

X6: Composition of the Tertiary Industry-Value-added of 

the Tertiary Industry (%). 

Y: Per Capita Disposable Income of Zhejiang Province (1 

yuan). 
Note: When selecting explanatory variables, taking into 

account the impact of the overall macroeconomy on the 

regional economy and the universality of macroeconomic 

policies, the national overall indicators are selected for some 

indicators. 

2.2. Descriptive Statistics  

Firstly, the concentration trend and dispersion degree of the 

data are generalized, and the overall characteristics of the data 

are understood by calculating the minimum, maximum, mean 

and standard deviation of the explanatory variables and the 

explained variable, and the results are shown in Table 2. 

Table 2. Descriptive statistics of major variables 

Variables Min Max  Mean SD 

X1  10954.7 180817.5 72563.36 56607.79 

X2 95 826.51 513.5 239.71 

X3 5027 30307 15765.36 8245.12 

X4  100280.1 1143670 499192 332440.7 

X5 12581.51 172730.5 83955.5 55074.23 

X6  39.8 54.5 46.37 5.08 

Y 9721 57541 28877 14139.89 

 

As can be seen from Table 2, among the three explanatory 

variables X1, X4 and X5 with the same unit (100 million 

yuan), the range and standard deviation of X4 are the largest, 

and the range and standard deviation of X1 and X5 are 

relatively small, indicating that the dispersion degree of X4 is 

high, and the dispersion degree of X1 and X5 is relatively low. 

In addition, the mean and standard deviation of the 

explanatory variable Y (i.e., disposable income of residents in 

Zhejiang Province) are 28877 and 14139.89, respectively, 

indicating that the average disposable income of residents in 

Zhejiang Province from 2000 to 2021 is 28877 yuan, and 

there are large differences in the annual disposable income of 

residents in different years. 

Next, a longitudinal comparison of the disposable income 

of residents in Zhejiang Province in the past 20 years is made, 

and a line chart is drawn, as shown in Figure 1. 

 



 

255 

 
Figure 1. Line chart of disposable income between Zhejiang Province and China from 2000 to 2021 

 
As can be seen from Figure 1, the disposable income of 

residents in Zhejiang Province has been steadily increasing 

from 2000 to 2021, and it is higher than the overall national 

level. There are three main reasons for this.  

First of all, from the perspective of industrial status, almost 

every city in Zhejiang Province has its own pillar industry. 

For example, Hangzhou, the capital of the Internet, Huzhou, 

the capital of brushes, Yiwu, the capital of small commodities, 

and so on.  

Secondly, from the perspective of geographical location, 

Zhejiang Province is located in the southeast coastal area of 

China, with convenient water transportation, many ports and 

frequent foreign trade; And the terrain is relatively flat, which 

is conducive to the construction of transportation and perfect 

transportation facilities. At the same time, the southeast 

coastal area, as a pioneer area of reform and opening up, is 

supported by relevant national policies.  

In addition, private enterprises in Zhejiang Province have a 

long history of development and good development status, 

and private entrepreneurs have a strong spirit of innovation. 

The development of the private economy in Zhejiang 

Province started in the early 80s of the 20th century, and has 

roughly experienced the initial development stage in the early 

stage of reform and opening up, the rapid development stage 

in the mid-to-late 80s of the 20th century, the comprehensive 

development stage after the 14th National Congress of the 

Communist Party of China, the improvement development 

stage after the 15th National Congress of the Communist 

Party of China, and the transformation and upgrading stage 

after the 17th National Congress of the Communist Party of 

China. After these stages of improvement and development, 

private enterprises in Zhejiang Province have realized the leap 

from agricultural economy to industrial economy. 

Next, the disposable income of residents in each 

provincial-level administrative region in the same year (2022) 

is compared horizontally and a bar chart is drawn, as shown 

in Figure 2.

 

 
Figure 2. Disposable income of residents in 2022 by provincial-level administrative region 

 
As can be seen from Figure 2, the top three provincial-level 

administrative regions in terms of disposable income in 2022 

are Shanghai, Beijing, and Zhejiang.  

As the largest economic center in China, Shanghai has the 

same advantages as an international metropolis. First of all, 

historically, the Shanghai metropolitan area has been highly 

prosperous since ancient times, since the construction of the 

Grand Canal in the Sui Dynasty, China's economic hinterland 

has been transferred from the Central Plains including 

Chang'an and Luoyang to the Jiangnan region, the Southern 

Song Dynasty began to become a highly prosperous place for 

world commerce, Suzhou became the national economic 

0

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Beijing, 77415

Tianjin, 48976

Heibei, 30867Shanxi, 29178
Neimenggu, 35921Liaoning, 36089

Jilin, 27975Heilongjiang, 28346

Shanghai, 79610

Jiangsu, 49862

Zhejiang, 60302

Anhui, 32745

Fujian, 43118

Jiangxi, 32419
Shandong, 37560

Henan, 28222
Hubei, 32914Hunan, 34036

Guangdong, 47065

Guangxi, 27981Hainan, 30957
Chongqing, 35666

Sichuan, 30679
Cuizhou, 25508Yunnan, 26937Tibet, 26675

Shaanxi, 30116
Gansu, 23273
Qinghai, 27000Ningxia, 29599Xinjiang, 27063

0

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30000

40000

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90000



 

256 

center during the Ming and Qing dynasties, and the modern 

economic center of China was transferred from Suzhou to 

Shanghai. Secondly, Shanghai has strong industrial strength, 

and its industrial clusters and industrial chains rank among the 

top in the country and even the world. Therefore, in 2022, the 

disposable income of Shanghai residents ranked first among 

all provincial-level administrative regions.  

As the capital of China, Beijing ranked second in terms of 

disposable income in 2022. In terms of economic advantages, 

Beijing is one of China's economic centers, with many 

headquarters of state-owned and private enterprises. In terms 

of educational advantages, Beijing has many well-known 

higher education institutions, such as Tsinghua University and 

Peking University. These universities have an important 

position in China and even in the world, attracting many 

domestic and foreign students to study and research, and 

cultivating many high-quality talents. As a result, in 2022, the 

disposable income of residents in Beijing ranked second 

among all provincial-level administrative regions.  

In 2022, Zhejiang Province ranked third among provincial-

level administrative regions in terms of disposable income, 

for the following reasons. First, Zhejiang Province has a 

profound historical and cultural heritage. Zhejiang has been a 

prosperous place in our country since ancient times, and "the 

southeast shape wins, the three Wu capitals, and Qiantang has 

been prosperous since ancient times" refers to the area of 

Zhejiang. Second, Zhejiang Province has outstanding people, 

celebrities, since ancient times is a place of developed 

education in China, the quality of the population is high, the 

number of champions from Zhejiang in ancient times is 

second only to Jiangsu, after the founding of New China, the 

number of academicians from Zhejiang and the Chinese 

Academy of Sciences is second only to Jiangsu. Third, 

Zhejiang Province has superior geographical location and 

natural environment. Zhejiang Province is located in the 

southeast coast of China, is the forefront of China's reform 

and opening up, by the north of Zhejiang, Shanghai, southern 

Jiangsu Yangtze River Delta is one of the most economically 

developed regions in China. Fourth, the diligent spirit and the 

characteristics of Zhejiang people who are good at doing 

business are also one of the reasons. 

2.3. Analysis of Influencing Factors Based On 
Multiple Linear Regression  

2.3.1. Correlation Analysis  
The correlation coefficient is a statistic calculated from the 

sample data that measures the strength of the linear 

relationship between two variables. Table 3 shows the 

Pearson correlation coefficient matrix between the six 

explanatory variables and one explanatory variable (i.e., the 

per capita disposable income of residents in Zhejiang 

Province).  

 

 

Table 3. Pearson correlation coefficient matrix 

 X1 X2 X3 X4 X5 X6 Y 

X1 1 0.9 0.99 0.99 0.98 0.98 1 

X2 0.9 1 0.94 0.92 0.95 0.88 0.92 

X3 0.99 0.94 1 1 1 0.98 1 

X4 0.99 0.92 1 1 0.99 0.97 1 

X5 0.98 0.95 1 0.99 1 0.97 0.99 

X6 0.98 0.88 0.98 0.97 0.97 1 0.98 

Y 1 0.92 1 1 0.99 0.98 1 

 
As can be seen from Table 3, there is a high positive 

correlation between the influencing factors and the per capita 

disposable income of residents. 

2.3.2. Multicollinearity Test 
The multiple linear regression model was established by 

the traditional least squares method, and the variance 

expansion factor of each explanatory variable was 

calculated 𝑉𝐼𝐹𝑖 = 1 ∕ (1 − 𝑅𝑖
2), see Table 4. Typically, if the 

VIF is greater than 10, it is considered to have severe 

multicollinearity. 

Table 4. Variance expansion factor 

 X1 X2 X3 X4 X5 X6 

x 729.32 362.54 3279.09 1993.41 513.19 122.16 

 

As can be seen from Table 4, there is a serious 

multicollinearity between the variables. 

2.3.3. Model Construction and Analysis  
Multiple linear regression analysis focuses on the 

quantitative relationship between variables, and describes this 

relationship through certain mathematical expressions, and 

then determines the degree of influence of changes in several 

variables (independent variables) on another specific variable 

(dependent variable), which is an effective method to explore 

the correlation and dependence between variables. The 

population regression model reflects the general trend of the 

relationship between variables, and the linear population 

regression model is the most common population regression 

model because it is simple in form and relatively easy to 

estimate and test parameters. The general form of a multiple 

linear regression model is: 

𝑌 = 𝛽0 + 𝛽1𝑋1 + 𝛽2𝑋2 + … + 𝛽𝑘 𝑋𝑘 + 𝜀  (i = 1,2, … , n) 

Where Y is the explanatory variable, 𝑋1, 𝑋2, … , 𝑋𝑘  is the 
explanatory variable, 𝛽0, 𝛽1, 𝛽2, … , 𝛽𝑘  is the parameter to 
be estimated, i.e., the regression coefficient; ε is a random 

error term; k is the number of explanatory variables; i is the 

subscript of the observation; n is the sample size.  

Due to the different dimensions of these six explanatory 

variables (the units are not uniform among them), in order to 

eliminate the influence of dimensions on the analysis results, 

the original data were normalized to eliminate the dimensions 

before multiple linear regression analysis, as shown in Table 

5. 

 

 

 

 

 

 

 

 

 



 

257 

 

Table 5. Normalized data 

Year X1 X2 X3 X4 X5 X6 Y 

2000 -1.09 -1.75 -1.30 -1.20 -1.30 -1.29 -1.18 

2001 -1.07 -1.71 -1.26 -1.17 -1.25 -1.02 -1.15 

2002 -1.04 -1.58 -1.17 -1.14 -1.20 -0.82 -1.10 

2003 -1.01 -1.36 -1.11 -1.09 -1.16 -0.86 -1.06 

2004 -0.97 -1.14 -1.03 -1.01 -1.09 -1.02 -0.99 

2005 -0.92 -0.86 -0.93 -0.94 -1.00 -1.00 -0.92 

2006 -0.85 -0.57 -0.84 -0.84 -0.89 -0.90 -0.84 

2007 -0.76 -0.27 -0.68 -0.69 -0.70 -0.68 -0.70 

2008 -0.66 -0.01 -0.52 -0.54 -0.54 -0.68 -0.57 

2009 -0.57 0.07 -0.39 -0.45 -0.44 -0.39 -0.47 

2010 -0.45 0.26 -0.24 -0.26 -0.20 -0.43 -0.32 

2011 -0.22 0.40 -0.03 -0.03 0.11 -0.41 -0.12 

2012 -0.03 0.46 0.16 0.12 0.30 -0.17 0.08 

2013 0.36 0.52 0.33 0.28 0.48 0.10 0.25 

2014 0.53 0.61 0.51 0.43 0.64 0.38 0.44 

2015 0.70 0.70 0.68 0.57 0.74 0.87 0.61 

2016 0.84 0.80 0.89 0.74 0.84 1.19 0.80 

2017 1.01 0.93 1.05 1.00 1.10 1.24 1.01 

2018 1.22 1.00 1.25 1.26 1.32 1.36 1.23 

2019 1.44 1.02 1.49 1.47 1.34 1.56 1.48 

2020 1.62 1.18 1.36 1.55 1.28 1.60 1.62 

2021 1.91 1.31 1.76 1.94 1.61 1.36 1.91 

In this paper, we collected various relevant indicators of 

Zhejiang Province from 2000 to 2021, and established a 

multiple regression model with per capita disposable income 

Y as the explanatory variable and six indicators in Table 1 as 

the explanatory variables: 

Y=β0+β1X1+β2X2+β3X3+β4X4+β5X5+β6X6+ε        

Among them, the ε is a random error term, which obeys a 

normal distribution and is independent of each other, i.e., ε~N 

(0, σ2).  

Next, multiple linear regression analysis was performed in 

Excel (with a specified significance level of 0.05) and the 

output results are shown in Table 6. 

 

Table 6. Regression analysis results output by Excel 

Regression statistics 

Multiple R 0.996966 

R Square 0.993941 

Adjusted R Square 0.992515 

Standard error 1223.306 

Observations 22 

analysis of variance 

  df SS MS F Significance F 

Regression 

analysis 
6 21.00090124 3.500150207 18789.6218 3.43354E-28 

Residuals 15 0.002794216 0.000186281   

Total 21 21.00369545       

Standard error 

  Coefficients  t Stat P-value Lower 95% Upper 95% 

Intercept 0.00053725 0.00291066 0.184579968 0.856030861 0.00566668 0.006741175 

X Variable 1 0.267078769 0.047030943 5.678788275 4.37649E-05 0.16683469 0.367322852 

X Variable 2 0.032738659 0.015964622 2.050700607 0.058191624 0.00128913 0.066766445 

X Variable 3 0.004079179 0.088285302 0.046204504 0.963756789 0.18409649 0.192254846 

X Variable 4 0.760605869 0.061744132 12.31867458 3.01878E-09 0.62900137 0.892210372 

X Variable 5 -0.13685076 0.041048063 3.333915197 0.004532443 0.22434263 0.049358885 

X Variable 6 0.07630142 0.019614641 3.890023815 0.001450449 0.0344938 0.118109037 

 
The following conclusions can be drawn from Table 6:  

Firstly, through the regression statistics table in the Excel 

output results, it can be seen that the value of the R Square 

(decision coefficient) of the model is 0.993941, which is close 

to 1, indicating that the model has a good fitting effect on the 

sample data. Secondly, from the table of regression 

parameters in the Excel output results, it can be seen that the 

estimated multiple linear regression equation is: 

𝑌 = 0.00053725 + 0.267078769𝑋1 + 0.032738659𝑋2
+ 0.004079179𝑋3
+ 0.760605869𝑋4−0.13685076𝑋5
+ 0.07630142𝑋6 

From this equation, it can be seen that X1, X2, X3, X4 and 

X6 are positively correlated with the disposable income of 



 

258 

residents in Zhejiang Province, and X5 is negatively 

correlated with the disposable income of residents in Zhejiang 

Province, that is, the higher the wages of urban employees in 

Zhejiang Province, the higher the number of graduates from 

ordinary colleges and universities, the higher the per capita 

consumption expenditure of urban residents, the higher the 

GDP of Zhejiang Province, and the more the added value of 

the tertiary industry, the higher the disposable income of 

residents in Zhejiang Province. Conversely, the higher the tax 

revenue, the lower the disposable income of residents in 

Zhejiang Province. Therefore, the relevant government 

departments can vigorously develop the national economy 

and higher education by adjusting the industrial composition, 

and adjust the tax structure to promote the increase of 

residents' income.  

In addition, the analysis of variance table and regression 

parameter table in the Excel output results were used to test 

the linear relationship and regression coefficient, respectively. 

Firstly, the significance test of the linear relationship was 

performed. The first step is to formulate a hypothesis, 

H0: 𝛽1 = 𝛽2 = 𝛽3 = 𝛽4 = 𝛽5 = 𝛽6 (the linear relationship 
between the explanatory variable and the explanatory variable 

is not significant); H1: 𝛽1, 𝛽2, 𝛽3, 𝛽4, 𝛽5, 𝛽6 at least one of 
them is not 0 (the linear relationship between the explanatory 

variable and the explanatory variable is significant). In the 

second step, determine the test statistic F; the third step is to 

make statistical decisions. According to the ANOVA table, F 

Significance=3.43354E-28 is less than 0.05, rejecting null 

hypothesis H0 and accepting alternative hypothesis H1, 

indicating that the linear relationship between the explanatory 

variable and the explanatory variable is significant, but it does 

not mean that the relationship between the disposable income 

of residents in Zhejiang Province and each explanatory 

variable is significant.  

Then, the significance of the regression coefficient was 

tested. The first step is to put forward the hypothesis, H0: 
𝛽𝑖 = 0 (the explanatory variable has no significant effect on 
the explanatory variable), H1: 𝛽𝑖 ≠ 0  (the explanatory 
variable has a significant effect on the explanatory variable), 

the second step is to determine the test statistic t, and the third 

step is to make a statistical decision. From the regression 

parameter table, it can be seen that the P values corresponding 

to 𝛽1, 𝛽4, 𝛽5, 𝛽6 are less than 0.05, indicating that the four 
explanatory variables X1, X4, X5, and X6 have a significant 

impact on the disposable income of residents in Zhejiang 

Province. The P value corresponding to 𝛽2, 𝛽3 is greater than 
0.05 and fails the test, but this does not necessarily mean that 

the impact of X2 and X3 on the disposable income of 

residents in Zhejiang Province is not significant, because this 

may be due to multicollinearity. 

3. Conclusions and Policy 
Recommendations  

3.1. Conclusion  

This paper selects the disposable income of residents in 

Zhejiang Province as a specific economic indicator, and 

selects six variables that can explain the economic index, 

firstly uses descriptive statistics to summarize the data, and 

compares the disposable income of residents horizontally and 

vertically. Then, multiple linear regression analysis was used 

to obtain the regression analysis results, and the significance 

test was performed. The results show that: (1) the disposable 

income of residents in Zhejiang Province gradually increased 

from 2000 to 2021 and was higher than the national level; (2) 

In 2022, the disposable income of residents in Zhejiang 

Province ranked third among provincial-level administrative 

regions in China; (3) There was a positive correlation between 

the wages of urban employees, the number of graduates from 

ordinary colleges and universities, the per capita consumption 

expenditure of urban residents, the GDP of Zhejiang Province 

and the added value of the tertiary industry and the disposable 

income of residents in Zhejiang Province. There is a negative 

correlation between taxation and disposable income of 

residents in Zhejiang Province. (4) The linear relationship 

between the six explanatory variables and the disposable 

income of residents in Zhejiang Province was significant. (5) 

The four explanatory variables of total wages of urban 

employees in Zhejiang Province, GDP of Zhejiang Province, 

tax revenue and added value of the tertiary industry have a 

significant impact on the disposable income of residents in 

Zhejiang Province.  

3.2. Policy Recommendations  

First, government departments should increase residents' 

disposable income by promoting employment, continuously 

adjusting and optimizing the industrial structure, and 

adjusting tax policies, promote sustained, healthy and high-

quality economic development, and strive to build a modern 

socialist country.  

Second, Zhejiang Province should continue to give full 

play to its historical and geographical advantages, strengthen 

all-round cooperation with the Yangtze River Delta urban 

agglomeration, support the development of private 

enterprises, and promote the development of higher education, 

so as to achieve the organic unity of economic, cultural and 

social construction.  

Third, entrepreneurs in Zhejiang Province should continue 

to carry forward the entrepreneurial spirit and innovative 

spirit and devote themselves to the construction of a new era 

of socialism with Chinese characteristics. 

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