With the intensification of social competition, the rate of return to education is gradually decreasing. This leads to a reduction in the indirect effect of education on happiness. This article uses the bivariate ordered Probit model under the educational field theory to investigate the impact of higher education innovation on social class and residents’ happiness. The results show that the innovation and reform of higher education will have a certain impact on residents’ happiness. The improvement of education level can significantly improve the individual's subjective wellbeing, and there are significant urbanrural differences. This shows that education reform and innovation are imperative.
Keywords
 higher education
 hierarchical and orderly probit
 semiparametric estimation
 innovation and reform
MSC 2010
 47D06
As the old saying goes, ‘everything is inferior, only reading is high.’ It can be seen that receiving education in Chinese traditional culture is a noble and beautiful thing. Only by passing the imperial examination can the poor students in ancient times leave the bottom of society and realize their ideals in life. Since the founding of New China, especially after the college entrance examination reform in 1977, Chinese education has developed rapidly [1]. From a qualitative point of view, Chinese per capita years of education reached 10.05 years in 2014, compared with 6.38 years in 1985. In terms of quantity, the number of illiterate Chinese has dropped from 22.23% 20 years ago to 4%. Chinese social civilization has reached a new height with the development of education, and social values have also undergone great changes. The spiritual pursuit has received more and more attention. The topic of ‘happiness’ remains high.
Education in China entrusts the hope of hundreds of millions of families for a better life and shoulders the ultimate task of the personal pursuit of a happy life. On the one hand, education can improve personal selfcultivation and give people a sense of spiritual happiness [2]. On the other hand, people's happiness is indirectly improved through objective conditions such as rising income levels and social status. However, as social competition intensifies, the rate of return to education decreases. This leads to a reduction in the indirect effect of education on happiness. According to the ‘National Happiness Report 2014’ released by the China Family Finance Research and Research Center, the highest happiness index is for primary school graduates. PhDs’ happiness is not even as good as that of illiterate students. How to effectively guide and correctly play the function and role of education in social development and transformation is very important.
Since the publication of Easterlin's article on the relationship between changes in American intertemporal income and changes in subjective wellbeing in 1974, subjective wellbeing has gradually attracted a large number of economists. Subjective wellbeing has always been an important subject in sociology and psychology, and the impact of education on wellbeing has always been a concern [3]. There are different opinions in the literature on the influence of education level on residents’ happiness. Some scholars believe that education can improve individual subjective wellbeing, and some literature believes that education does not affect subjective wellbeing) or even has a negative effect. Some scholars have found that people with secondary education have the highest level of happiness, not those with higher education. This may be because people with a high level of education have higher expectations of the future, and subjective wellbeing will decrease when there is a large gap between reality and expectations. Some scholars have explored the impact of urban residents’ educational level on income and subjective wellbeing. When the income level is not controlled, the education level has a positive effect on happiness. Once the income level is controlled, the education level harms happiness. Therefore, it is concluded that the effect of education level on happiness is mainly realized by increasing income level. Some scholars use survey data of residents in 50 states in the United States to study the impact of education, health, and income on residents’ wellbeing [4]. The study found that subjective wellbeing and education level still have significant positive effects after controlling income and health.
The article uses the semiparametric estimation method of the ordered probit model to test education. It is based on the 2014 Chinese Family Tracking Survey (CFPS2014) data on the impact of individual residents’ subjective wellbeing and urbanrural differences. The article explores its influence mechanism.
To examine the impact of education on residents’ happiness, we used the 2014 China Family Tracking Survey (CFPS) data to conduct empirical research. The CFPS survey questionnaire has four main questionnaire types: community questionnaire, family questionnaire, adult questionnaire, and child questionnaire. We consider that the article mainly studies happiness [5]. Minors’ evaluation of their wellbeing has different influencing factors from adults. Therefore, this article limits the sample scope to adults, and 27,992 valid samples are obtained after screening.
First of all, the main research object of the article is the education level and happiness of residents. We measured the level of education based on the answer to the question ‘When did you leave school last?’ in the CFPS questionnaire to obtain the variable education level. The number of people with junior high school education and below accounted for 79.46% in the sample. It can be said that those with high school education and above belong to the higheducated population. Therefore, this article constructs another indicator of education level to measure education level. When the respondent's education is high school and above, it is recorded as 1, and the rest is 0. The measure of residents’ subjective wellbeing comes from the question, ‘how happy do you feel about yourself.’ Respondents scored 1–10. The higher the score, the stronger the sense of happiness. It can be seen from Figure 1a that the overall happiness level of the interviewed residents is relatively high. When happiness is relatively low, the proportion of those with a high school education or lower is higher than those with a high school education or above [6]. This shows that people with low education are more likely to feel unhappy when the happiness score is higher, significantly more people with a high school degree or above than those with a high school degree or less. However, among those who feel very happy (10 points), more people with a high school degree or less than those with a high degree. Taking into account that the happiness score is too subjective, and there may be reliability bias in the precise stratification, we divide the 10 points into three segments: 1 to 4 points to indicate dissatisfaction, 5 to 7 points to indicate fairness, and 8 to 10 points to indicate very satisfied. It can be seen from Figure 1b that the majority of people with a high sense of happiness are still those with a high degree of education.
In addition, to accurately judge the impact of education on residents’ happiness, a more indepth model analysis is needed because of the many factors that affect residents’ happiness. Factors include education, urban and rural areas, gender, age, marriage, health, income, work status, etc. These factors can represent personal characteristics and have important effects on happiness. Descriptive statistics are shown in Table 1.
Descriptive statistics of each variable
2.523  0.621  1  3  
47.832  15.78  16  99  
0.489  0.5  0  1  
2.5  1.304  1  8  
0.837  0.369  0  1  
3.015  1.247  1  5  
0.739  0.439  0  1  
2.939  0.999  1  5  
3.44  1.226  1  5  
8799.7  19125.7  0  442,000 
The ordered probit model uses observable ordered response data to establish a model to study the changing law of unobservable latent variables. It is a special case of the restricted dependent variable model [7]. The happiness degree variable studied in this article has no specific sample data, so it is also a kind of latent variable, and its influence equation is expressed in linear form as follows.
Regarding the distribution of as unknown, the Hermit sequence
If the degree of nonemployment education is not in the research sample, the decisionmaking selection of education participation will lead to sample selection problems [9]. To eliminate potential sample selection bias, an ordered sample selection model should be used to estimate the impact of various factors on happiness. Suppose the education participation equation is:
When
Similar to the semiparametric estimation method of Probit model (3), the semiparametric estimation of sample selection ordered Probit model (3) and (5) regards the joint distribution of and as unknown. We use the Hermite function sequence.
Considering that happiness is an ordered dependent variable, we use an ordered probit model to estimate. To ensure the consistency of parameter estimation, we use the Hermit sequence to simulate the residual distribution to modify the ordered Probit model. This results in a consistent estimate of the parameters.
We use a semiparametric estimation method based on the ordered probit model to test the impact of education on happiness. When k
Semiparametric ordered probit model estimation results
0.102*** (0.013)  0.119*** (0.016)  0.079*** (0.016)  0.145*** (0.021)  
0.214*** (0.028)  0.234*** (0.030)  
−0.055*** (0.005)  −0.059*** (0.007)  −0.052*** (0.006)  
0.001*** (0.000)  0.001*** (0.000)  0.001*** (0.000)  0.001*** (0.000)  
−0.105* (0.044)  
Control  Control  Control  Control  
−22717.567  −22714.489  −9861.211  −12831.042  
32.666  32.193  17.315  34.220  
0.000  0.000  0.000  0.000  
1.465  1.465  1.476  1.521 
Results (1) and (2) are estimated results for the entire sample. Results (1) show that the improvement of education level can significantly promote residents’ happiness, and there are significant differences in happiness between urban and rural residents. Due to Chinese longterm urbanrural dual structure, urban and rural residents show significant differences in economic conditions and ideological concepts. So is there a significant difference in the effect of education on the happiness of urban and rural residents? We add the intersection of education and town, as shown in result (2). The coefficient of the cross term is negative, and the test is significant [12]. This shows that education is not as effective in improving the happiness of urban residents as it does for rural residents. This may be because the popularization of education for urban residents is earlier than that for rural residents, and the popularity is also higher. According to the sample data, the proportion of rural senior high school students and above accounted for 10.3%, while the proportion in urban areas was 32.05%. According to the principle of vagueness, the scarcity of high education makes rural residents happier than urban residents.
Further estimates are made on the urban and rural samples separately. Results (3)(4) are estimated results using OP nonparametric model. It can be seen that the coefficient of the education variable in the urban sample is about half of the coefficient in the rural sample, and there is a big difference between the two.
From the results of the above model, it can be seen that education can significantly improve individuals’ subjective wellbeing. For those who wish to receive education, education itself is happy. Education is willing to improve people's cognition, helps cultivate correct world outlook, values, and enhances selfevaluation of happiness. On the other hand, existing research proves that education indirectly impacts happiness through economic and other factors. From the description of sample statistics, we can see that the proportion of people with high school education and above is only 20.54%. We classify this type of people as those who have received higher education [13]. We mainly explore the mechanism of higher education's influence on individual subjective wellbeing and still adopt the nonparametric estimation of the orderly corresponding probit model. It can be seen from the previous results that this method is superior to ordinarily ordered probit. The results of judging the impact path by introducing crossterms are shown in Table 3.
The estimation result (5) uses the binary variable higher education (edu2) instead of education (Edu), and the coefficient is still significantly positive. This also shows the robustness of the model. The estimation results (6)–(9) were added to the cross items of education and income (logarithm), health, work status, and status, and the statistical tests of each cross item were significant. The coefficient and significance of higher education in the estimation result (6) have not changed much. This shows that higher education cannot promote the individual happiness of residents by increasing personal income. As a result, the signs of higher education coefficients of (7), (8), and (9) became negative and no longer significant. This shows that higher education can improve the individual's subjective wellbeing by improving the individual's health, work status, and status.
Introducing crossterms to determine the impact path results
0.162*** (0.031)  0.117** (0.036)  −0.005 (0.036)  −0.016 (0.036)  −0.078* (0.039)  
0.249*** (0.029)  0.245*** (0.029)  0.219*** (0.028)  0.223*** (0.028)  0.224*** (0.028)  
−0.055*** (0.005)  −0.055*** (0.005)  −0.055*** (0.005)  −0.053*** (0.004)  −0.055*** (0.005)  
0.001*** (0.000)  0.001*** (0.000)  0.001*** (0.000)  0.001*** (0.000)  0.001*** (0.000)  
Control  Control  Control  Control  Control  
0.004* (0.002)  
0.026*** (0.004)  
0.107*** (0.015)  
0.039*** (0.005)  
−22755.746  −22753.389  −22731.603  −22724.076  −22715.474  
35.960  33.352  38.652  350545  28.105  
0.000  0.000  0.000  0.000  0.000  
1.453  1.448  1.447  1.451  1.442 
The article uses the semiparametric estimation method of the ordered corresponding Probit model to test the impact of education on individual subjective wellbeing empirically. The estimation results of the model show that the improvement of education level can significantly improve the individual's subjective wellbeing, and there are significant urbanrural differences. The improvement of rural residents’ happiness is significantly bigger than that of urban residents. Further research has shown that improving health, getting a job, and improving social status are the three channels for higher education to increase residents’ wellbeing. Higher education cannot improve residents’ happiness by increasing income.
Semiparametric ordered probit model estimation results
0.102 
0.119 
0.079 
0.145 

0.214 
0.234 

−0.055 
−0.059 
−0.052 

0.001 
0.001 
0.001 
0.001 

−0.105 

Control  Control  Control  Control  
−22717.567  −22714.489  −9861.211  −12831.042  
32.666  32.193  17.315  34.220  
0.000  0.000  0.000  0.000  
1.465  1.465  1.476  1.521 
Descriptive statistics of each variable
2.523  0.621  1  3  
47.832  15.78  16  99  
0.489  0.5  0  1  
2.5  1.304  1  8  
0.837  0.369  0  1  
3.015  1.247  1  5  
0.739  0.439  0  1  
2.939  0.999  1  5  
3.44  1.226  1  5  
8799.7  19125.7  0  442,000 
Introducing crossterms to determine the impact path results
0.162 
0.117 
−0.005 (0.036)  −0.016 (0.036)  −0.078 

0.249 
0.245 
0.219 
0.223 
0.224 

−0.055 
−0.055 
−0.055 
−0.053 
−0.055 

0.001 
0.001 
0.001 
0.001 
0.001 

Control  Control  Control  Control  Control  
0.004 

0.026 

0.107 

0.039 

−22755.746  −22753.389  −22731.603  −22724.076  −22715.474  
35.960  33.352  38.652  350545  28.105  
0.000  0.000  0.000  0.000  0.000  
1.453  1.448  1.447  1.451  1.442 
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