# The Intergenerational Income Mobility of Chinese Urban and Rural Residents Based on Big Data Technology

Authors: Yingying Shao
Journal: Computational Intelligence and Neuroscience
 (2022)

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## Abstract

In order to analyze the intergenerational income mobility of urban and rural residents, this paper uses big data technology to study the intergenerational income mobility of urban and rural residents in China, constructs a corresponding model, and presents an interval type-2 fuzzy model based on the distributed integration method. The model constructs an initial interval type-2 fuzzy model through a distributed integration strategy and provides an optimization method for the parameters of the interval type-2 fuzzy model based on the least squares method, which is used to ensure the prediction performance of the model. The experimental study verifies that the big data technology can play an important role in the research on the intergenerational income of urban and rural residents in China.


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## Body

## 1. Introduction

At this stage, scholars have reached a basic consensus on the intrinsic relationship between the inequality of opportunity and the inequality of outcomes and believe that the inequality of opportunity is the fundamental cause of all other inequalities and is the greatest inequality. The reason is that the inequality of opportunity brings about a person’s inability to change his income status and social status through efforts, and often the external environment and other factors inherently determine the individual’s income distribution status. Among the related methods for measuring income gap, the Lorenz curve, Gini coefficient, and Theil index are more commonly used, but these methods only involve the measurement and evaluation of the equality of income distribution results.Intergenerational mobility has three different meanings, according to literature [1]. The first describes how a given object (such as education or employment) moves from one generation to the next in relative position, the second gauges opportunity equality, and the third gauges luck in life. The degree to which the relative position of income varies from one generation to the next is referred to as intergenerational income mobility, according to the initial definition of the term. The other two interpretations—in which the intergenerational mobility of income is utilised as a key metric to assess the equity of intergenerational income—are the topics of this essay. The indicator of intergenerational income elasticity, or the elasticity of the children’s income to the parents’ income, is often used to measure the intergenerational income flow. Intergenerational income elasticity refers to the degree to which a person’s status in the overall income distribution is determined by the status of the previous generation, that is, the degree to which the income of the parents affects the income of the children. More precisely, it is the elasticity of the income of the children to the income of the parents. The intergenerational income elasticity can also be considered as intergenerational income transmission, that is, the income of the parents can be passed on to the children through certain channels. The greater the intergenerational income elasticity of a society, the higher the dependence of children’s income on their parents, the lower the intergenerational income mobility, and the higher the social opportunity inequality. Conversely, the smaller the intergenerational income elasticity of a society, the lower the dependence of children’s income on their parents, the higher the intergenerational income mobility, and the lower the social opportunity inequality [2].The classical liberal view of equity focuses on the intergenerational mobility of income. Classical liberals advocate relatively equal opportunities in the process of market competition and oppose intervention in the outcome of income distribution. He believes that to achieve social justice, the most fundamental thing is to establish and implement a free market mechanism, rather than relying on the government’s redistributive policy [3]. Classical liberals mainly emphasize the realization of “fairness of opportunity” by relying on the free market order, while denying the realization of “fairness of outcome” by relying on the government’s redistributive policy. According to the fairness thought of classical liberals, we can see that in order to achieve fair income distribution, the government should improve the market mechanism for fair competition, give equal opportunities to all economic classes, especially the low-income class, and promote intergenerational income flow. This is done to achieve economic efficiency and growth and ultimately achieve fair outcomes [4]. Intergenerational mobility of income is also relevant to social justice. Two principles of justice are put forward: first, that everyone should have an equal right to the broadest and equal system of fundamental liberties consistent with a similar system of liberties enjoyed by all; second, social and economic unequal arrangements should enable them to serve the best interests of the most disadvantaged, as well as to be linked to positions and positions open to all under conditions of equal opportunity [5]. Literature [6] believes that the goal of the government should be to improve the welfare of the worst people in the society, and the following two principles should be followed to solve social and economic inequalities. The second is the principle of equal opportunity, which makes all positions and positions open to all. On the basis of the principle of equal opportunity, the principle of difference is used to allow the smallest beneficiaries in the society to receive the greatest compensation and benefits so as to try to reduce the degree of social inequality. According to Rawls’s two principles of justice, in order to promote the fairness of social opportunities, the government should formulate corresponding policies, on the one hand, giving the low-income people at the bottom of the income distribution the opportunity to move upward, and on the other hand, formulating policies to help them move upward, in order to improve the overall intergenerational income mobility, thereby reducing the transmission of income inequality between generations [7].In a culture where there is significant intergenerational income mobility and the economic standing of parents has no bearing on the future economic status of their offspring, everyone has an equal chance of achieving the desired results or equality of opportunity. According to some academics, the reason why people are so interested in intergenerational economic mobility is because it has to do with the opportunity equality for persons from various familial origins [8]. The authors in [9] analyse intergenerational income mobility using the equal opportunity paradigm. Two categories of factors are identified when investigating the issue of equality of opportunity: one is self-responsible, such as the effort level, and the other is non-self-responsible, such as familial background. The basic idea of the equality of opportunity is that the income gap due to the latter needs to be compensated for, and the income gap due to the level of individual effort is considered reasonable. Literature [10] illustrates the relationship between the equality of opportunity and the equality of outcome and their relationship with intergenerational income mobility from the perspective of social justice and concludes that to a large extent, the inequality of opportunity will determine outcome inequality of opportunity; the inequality of opportunity can be measured by the magnitude of intergenerational income mobility, that is, the equality of opportunity in a society can be measured by the extent to which parents’ income status determines the future income status of their children [11].Higher intergenerational income mobility can also boost economic growth. In a society with a high degree of mobility or openness, people can freely choose the career they want without being constrained by their own family background so as to be able to exert their own strengths; no matter what the starting state is, everyone has an equal chance to achieve the desired outcome, which increases motivation and contributes to economic growth. An elite society can achieve short-term prosperity but not long-term prosperity. The main reason is that in such a society with low intergenerational income mobility, the elites will set up barriers to hinder the intergenerational mobility of income to protect their vested interests. Ultimately, it leads to the waste of talents and the loss of resource allocation efficiency. On the one hand, the talents of people outside the elite class cannot be fully utilised. On the other hand, people outside the elite class cannot see the opportunity to improve their conditions and will suppress their effort positivity, leading to dynamic losses. A society with low intergenerational income mobility will deplete the power of long-term economic growth [12]. Literature [13] pointed out that intergenerational income mobility is the core of avoiding the “middle income trap” and achieving long-term economic growth. The reduction of intergenerational income mobility can easily make China fall into the “middle income trap,” so the government should take measures, such as the government’s investment should be transferred from physical capital investment to human capital investment such as education and health, and not only the total amount should be increased, but also they should focus on eliminating inequalities in education and health to gradually transform social structures and increase intergenerational income mobility to avoid entering the “middle income trap.”The authors in [14] used the established model to explain the relationship between the intergenerational income mobility of different countries and their intragenerational income inequality and found that in general, countries with high intergenerational income mobility have their income inequality. This would be lower; if two countries have the same intergenerational income mobility but different levels of income inequality, it is likely due to differences in the genetic process of their endowments. The authors in [15] pointed out that there is a positive correlation between the intergenerational income elasticity IGE and the degree of income inequality. The degree of income disparity increases with intergenerational income elasticity. Income mobility between generations may also be impacted by equality considerations. For instance, an increase in the rate of return to education would reduce income inequality and intergenerational economic mobility in the educational sector [16].This paper uses big data technology to study the intergenerational income mobility of Chinese urban and rural residents and builds a corresponding model to improve the analysis effect of intergenerational income mobility [17].


## 2. Construction Method of the Interval Type II Fuzzy Model Based on the Distributed Ensemble Method

### 2.1. The Overall Idea of Constructing the Interval Type-2 Fuzzy Model Based on the Distributed Integration Strategy

The overall framework of the above design method is shown in Figure1.Figure 1
Construction framework of the interval type-2 fuzzy model based on distributed integration.The specific process of the above method is(1)
The algorithm divides the training data setX into P training data subsets.(2)
The algorithm uses the ANFIS method to construct a type-1 fuzzy model corresponding toP training data subsets and obtains P type-1 fuzzy models.(3)
The algorithm integratesP type-1 fuzzy models to obtain the initial interval type-2 fuzzy model.(4)
The algorithm uses the least squares method to optimize the parameters of the interval type-2 fuzzy model to obtain the final model [18]. The realization way of the key link in this method will be given in the subsequent section.


### 2.2. Construction of the Type-1 Fuzzy Model Based on the Distributed Strategy

We assume that the initial training data set isxk,ykk=1N and divide the data set xk,ykk=1N into P training subdatasets, denoted as xpkp,ypkpkp=1Np. Among them, xk=x1k,x2k,⋯,xqkTand Nis the number of data in the training data set.The following will take thep-th training data subset xpkp,ypkpkp=1Np as an example to introduce the process of constructing its corresponding Type 1 fuzzy model.First, we determine that the domain of discourse of thes-th input variablexs of the p-th training data subset is U¯ps,U¯ps, and the upper and lower bounds of the domain of discourse are calculated as(1)U¯ps=minkp=1Npxskp,U¯ps=maxkp=1Npxskp.Furthermore, the type-1 fuzzy set used in the type-1 fuzzy division of the universe of discourse is a triangular fuzzy set, and the triangular fuzzy set is used to divide the universe of discourseU¯ps,U¯ps of the input variable xs. The specific division is shown in Figure 2.Figure 2
Type 1 fuzzy partition of input variablexs.In this type 1 fuzzy model division, the fuzzy division number of the input variablexs is ms, and the expression of the membership function of the i-th triangular fuzzy set As,pis,p is as follows:(2)μAs,pis,pxs=xs−as,pis,pbs,pis,p−as,pis,p,as,pis,p≤xs≤bs,pis,p,0,other,xs−cs,pis,pbs,pis,p−cs,pis,p,bs,pis,p≤xs≤cs,pis,p.Among them,(3)as,pis,p=es,pis,p−1,bs,pis,p=es,pis,p,cs,pis,p=es,pis,p+1.On this basis, the complete fuzzy rule base for the type 1 fuzzy model can be constructed for thep-th training data subset as follows:(4)Ri1,p,⋯,iq,p:x1isA1,pi1,p,x2isA2,pi2,p,⋯,xqisAq,piq,p⟶y=wi1,p,⋯,iq,p.Furthermore, the fuzzy rules in the fuzzy rule base are used as initial rules, and the ANFIS method is used to optimize these initial rules. Therefore, correspondingp type-1 fuzzy models can be constructed for the divided p training data subsets.


### 2.3. Construction of the Initial Interval Type-2 Fuzzy Model Based on the Integration Strategy

The obtainedp type-1 fuzzy models are integrated to construct the initial interval type-2 fuzzy model. The complete rule base is used for both type 1 and interval type-2 fuzzy models here.The integration strategy presented in this paper is shown in Figure3. In this strategy, firstly, p triangular type-1 fuzzy setsAs,1is,1,As,2is,2,⋯,As,pis,p in p type-1 fuzzy models are extracted, namely the upper part in Figure 3. Afterwards, p such triangular type-1 fuzzy sets are integrated to obtain trapezoidal type-2 fuzzy sets (as shown in the lower half of Figure 3).Figure 3
Integration of type 1 fuzzy sets.Thus, the rule base to obtain the initial interval type-2 fuzzy model can be expressed as(5)Ri1,⋯,iq:ifx1isA˜1i1,x2isA˜2i2,⋯,xqisA˜qiqthenyisw¯i1,⋯,iq,w¯i1,⋯iq.Among them,q is the number of input variables, w¯i1,⋯,iq,w¯i1,⋯iq is the interval weight of the consequent of rule i1,⋯,iq, and the interval weight is obtained by integrating the rule consequent of p type 1 fuzzy models, and its calculation expression is as follows:(6)w¯i1,⋯,iq=minp=1Pwi1,p,⋯,iq,p,w¯i1,⋯iq=maxp=1Pwi1,p,⋯,iq,p.


### 2.4. Parameter Optimization of the Interval Type-2 Fuzzy Model Based on the Least Squares Method

The distributed integration approach is used to create the initial interval type-2 fuzzy model. The vector form of the input-output relational expression of the same type II fuzzy model may be constructed using the output formula that was acquired after the reduction and defuzzification steps of the BMM approach.(7)y=fxTw.Among them,(8)fx=g¯1⋯1x⋯g¯m1⋯mqg¯1⋯1xg¯m1⋯mqxT,w=w¯1⋯1,⋯,w¯m1⋯mq,w¯1⋯1,w¯m1⋯mqT.In the formula,(9)g¯m1⋯mqx=αf¯m1⋯mqx∑i1=1m1⋯∑iq=1mqf¯i1⋯iqx,g¯m1⋯mqx=βf¯m1⋯mqx∑i1=1m1⋯∑iq=1mqf¯i1⋯iqx.For a given datasetxk,ykk=1N, the expected predicted output of the Type 2 fuzzy model should be consistent with the actual value output, that is, we want(10)fxkTw=yk.Among them,k=1,2,⋯N, and Nis the number of training data.That is to say, the regular interval weight vector of type II fuzzy obtained by training should make the deviation between the predicted value and the actual value as small as possible, that is,(11)minw∑fxkTw−ykk=12=minwXw−y22.Among them,(12)X=g¯1⋯1x1⋯g¯m1⋯mqx1g¯1⋯1x1⋯g¯m1⋯mqx1g¯1⋯1x2⋯g¯m1⋯mqx2g¯1⋯1x2⋯g¯m1⋯mqx2⋮⋮⋮⋮⋮⋮g¯1⋯1xN⋯g¯m1⋯mqxNg¯1⋯1xN⋯g¯m1⋯mqxN,y=y1y2⋯yNT.Therefore, the consequent parameters of the second type fuzzy model can be substituted by the least square method. The obtained least squares estimation of the interval weight vectorw of the regional type-2 fuzzy model is(13)w^=X+y.Among them,X+ is the generalized inverse of matrix X.We assume that the singular value decomposition ofX is(14)PHXQ=σ1⋱σr0⋱0.Among them,σ1,σ2,⋯,σr is the nonzero singular value of X and P=P1,P2,⋯,PNN×N,Q=q1|q2⋯qLL×L,L=2∏j=1qmj, where P and Q are matrices.The expression for the least squares solution ofw^ is(15)w^=∑i=1rPiHyσiqi.


## 2.1. The Overall Idea of Constructing the Interval Type-2 Fuzzy Model Based on the Distributed Integration Strategy

The overall framework of the above design method is shown in Figure1.Figure 1
Construction framework of the interval type-2 fuzzy model based on distributed integration.The specific process of the above method is(1)
The algorithm divides the training data setX into P training data subsets.(2)
The algorithm uses the ANFIS method to construct a type-1 fuzzy model corresponding toP training data subsets and obtains P type-1 fuzzy models.(3)
The algorithm integratesP type-1 fuzzy models to obtain the initial interval type-2 fuzzy model.(4)
The algorithm uses the least squares method to optimize the parameters of the interval type-2 fuzzy model to obtain the final model [18]. The realization way of the key link in this method will be given in the subsequent section.


## 2.2. Construction of the Type-1 Fuzzy Model Based on the Distributed Strategy

We assume that the initial training data set isxk,ykk=1N and divide the data set xk,ykk=1N into P training subdatasets, denoted as xpkp,ypkpkp=1Np. Among them, xk=x1k,x2k,⋯,xqkTand Nis the number of data in the training data set.The following will take thep-th training data subset xpkp,ypkpkp=1Np as an example to introduce the process of constructing its corresponding Type 1 fuzzy model.First, we determine that the domain of discourse of thes-th input variablexs of the p-th training data subset is U¯ps,U¯ps, and the upper and lower bounds of the domain of discourse are calculated as(1)U¯ps=minkp=1Npxskp,U¯ps=maxkp=1Npxskp.Furthermore, the type-1 fuzzy set used in the type-1 fuzzy division of the universe of discourse is a triangular fuzzy set, and the triangular fuzzy set is used to divide the universe of discourseU¯ps,U¯ps of the input variable xs. The specific division is shown in Figure 2.Figure 2
Type 1 fuzzy partition of input variablexs.In this type 1 fuzzy model division, the fuzzy division number of the input variablexs is ms, and the expression of the membership function of the i-th triangular fuzzy set As,pis,p is as follows:(2)μAs,pis,pxs=xs−as,pis,pbs,pis,p−as,pis,p,as,pis,p≤xs≤bs,pis,p,0,other,xs−cs,pis,pbs,pis,p−cs,pis,p,bs,pis,p≤xs≤cs,pis,p.Among them,(3)as,pis,p=es,pis,p−1,bs,pis,p=es,pis,p,cs,pis,p=es,pis,p+1.On this basis, the complete fuzzy rule base for the type 1 fuzzy model can be constructed for thep-th training data subset as follows:(4)Ri1,p,⋯,iq,p:x1isA1,pi1,p,x2isA2,pi2,p,⋯,xqisAq,piq,p⟶y=wi1,p,⋯,iq,p.Furthermore, the fuzzy rules in the fuzzy rule base are used as initial rules, and the ANFIS method is used to optimize these initial rules. Therefore, correspondingp type-1 fuzzy models can be constructed for the divided p training data subsets.


## 2.3. Construction of the Initial Interval Type-2 Fuzzy Model Based on the Integration Strategy

The obtainedp type-1 fuzzy models are integrated to construct the initial interval type-2 fuzzy model. The complete rule base is used for both type 1 and interval type-2 fuzzy models here.The integration strategy presented in this paper is shown in Figure3. In this strategy, firstly, p triangular type-1 fuzzy setsAs,1is,1,As,2is,2,⋯,As,pis,p in p type-1 fuzzy models are extracted, namely the upper part in Figure 3. Afterwards, p such triangular type-1 fuzzy sets are integrated to obtain trapezoidal type-2 fuzzy sets (as shown in the lower half of Figure 3).Figure 3
Integration of type 1 fuzzy sets.Thus, the rule base to obtain the initial interval type-2 fuzzy model can be expressed as(5)Ri1,⋯,iq:ifx1isA˜1i1,x2isA˜2i2,⋯,xqisA˜qiqthenyisw¯i1,⋯,iq,w¯i1,⋯iq.Among them,q is the number of input variables, w¯i1,⋯,iq,w¯i1,⋯iq is the interval weight of the consequent of rule i1,⋯,iq, and the interval weight is obtained by integrating the rule consequent of p type 1 fuzzy models, and its calculation expression is as follows:(6)w¯i1,⋯,iq=minp=1Pwi1,p,⋯,iq,p,w¯i1,⋯iq=maxp=1Pwi1,p,⋯,iq,p.


## 2.4. Parameter Optimization of the Interval Type-2 Fuzzy Model Based on the Least Squares Method

The distributed integration approach is used to create the initial interval type-2 fuzzy model. The vector form of the input-output relational expression of the same type II fuzzy model may be constructed using the output formula that was acquired after the reduction and defuzzification steps of the BMM approach.(7)y=fxTw.Among them,(8)fx=g¯1⋯1x⋯g¯m1⋯mqg¯1⋯1xg¯m1⋯mqxT,w=w¯1⋯1,⋯,w¯m1⋯mq,w¯1⋯1,w¯m1⋯mqT.In the formula,(9)g¯m1⋯mqx=αf¯m1⋯mqx∑i1=1m1⋯∑iq=1mqf¯i1⋯iqx,g¯m1⋯mqx=βf¯m1⋯mqx∑i1=1m1⋯∑iq=1mqf¯i1⋯iqx.For a given datasetxk,ykk=1N, the expected predicted output of the Type 2 fuzzy model should be consistent with the actual value output, that is, we want(10)fxkTw=yk.Among them,k=1,2,⋯N, and Nis the number of training data.That is to say, the regular interval weight vector of type II fuzzy obtained by training should make the deviation between the predicted value and the actual value as small as possible, that is,(11)minw∑fxkTw−ykk=12=minwXw−y22.Among them,(12)X=g¯1⋯1x1⋯g¯m1⋯mqx1g¯1⋯1x1⋯g¯m1⋯mqx1g¯1⋯1x2⋯g¯m1⋯mqx2g¯1⋯1x2⋯g¯m1⋯mqx2⋮⋮⋮⋮⋮⋮g¯1⋯1xN⋯g¯m1⋯mqxNg¯1⋯1xN⋯g¯m1⋯mqxN,y=y1y2⋯yNT.Therefore, the consequent parameters of the second type fuzzy model can be substituted by the least square method. The obtained least squares estimation of the interval weight vectorw of the regional type-2 fuzzy model is(13)w^=X+y.Among them,X+ is the generalized inverse of matrix X.We assume that the singular value decomposition ofX is(14)PHXQ=σ1⋱σr0⋱0.Among them,σ1,σ2,⋯,σr is the nonzero singular value of X and P=P1,P2,⋯,PNN×N,Q=q1|q2⋯qLL×L,L=2∏j=1qmj, where P and Q are matrices.The expression for the least squares solution ofw^ is(15)w^=∑i=1rPiHyσiqi.


## 3. Experimental and Comparative Analysis

To create five identical triangle-type fuzzy models, the method separates the training set into five equal subdatasets of the same size, each with a number of 1600. The method then combines the five type-1 fuzzy models it has gotten to create the first type-2 interval fuzzy model. To reach the final prediction outcome, the algorithm employs the least squares approach to optimize the interval type-2 fuzzy model's parameters.The experimental parameters of the BPNN, ANFIS, and T2FM-DE models are set as follows:(i)
In the BPNN model, the number of iterations is 10,000, and the activation function is the sigmoid function.(ii)
In the ANFIS model, Gaussian fuzzy sets are selected, and the number of fuzzy sets corresponding to each input variable is 3, and the number of iterations is 100.(iii)
In T2FM-DE, the interval type II fuzzy set is a Gaussian fuzzy set with an uncertain standard deviation value, and the number of iterations is 100.The results of the income prediction experiment are shown in Figure4.Figure 4
Results of the income forecast experiment (partial data) income (yuan/day).In order to facilitate the comparison of the prediction performance of the four models, the prediction results of the four models in the same time period are shown in Figure5.Figure 5
The experimental results of income prediction of the four models (partial data): (a) the model in this paper, (b) the T2FM-DE model, (c) BPNN model, and (d) ANFIS model.
(a)(b)(c)(d)In order to better illustrate the prediction performance of the model in this paper, the error distribution histograms of the four models are also given in Figure6. In this figure, the size of the error and the number of different error ranges can be used to illustrate the performance of the model in the experiment. From Figure 6, it can be observed that our model performs better in the income prediction experiment.Figure 6
The experimental results of income prediction of the four models: (a) the model in this paper, (b) the T2FM-DE model, (c) the BPNN model, and (d) the ANFIS model.
(a)(b)(c)(d)The above research verifies that the big data technology-based intergenerational income of Chinese urban and rural residents can play an important role in this paper, and the analysis is made on this basis.Based on the empirical results of this paper, the relevant policy recommendations of this study are as follows: first, it is necessary to increase government investment in education, increase the construction of educational resources, optimize fiscal expenditure, and reform the funding sharing system. Since education is an important guarantee for realizing social equity, so in order to achieve equity in opportunity, equity in education must be guaranteed. The investment is inadequate, much below the level of the rest of the world, and unable to satisfy the demands of a huge country like ours, which is the primary cause of the issues in our nation’s education. In addition, we must combine regional governments’ development duties to create a cohesive plan, put in place a fund-sharing mechanism that is mostly controlled by the province government, and establish schools with the greatest facilities, instructors, and teaching standards. We need to create more and better rural schools in undeveloped rural regions, enhance the welfare of rural teachers, and encourage more talented people to invest in education. The modernisation of education must be encouraged, and pupils must be taught using cutting-edge concepts and tools. In general, a person’s mind conforms to self-will, and only by allowing children to be themselves to the greatest extent can they develop their potential and break down barriers to development. We also need to develop various types of higher education, that is, to use technology to educate students. Today, there is no shortage of management talents in society, but talents with special skills are lacking. Increasing investment in some technical schools can alleviate social employment conflicts.Second, it is necessary to break down the barriers of the labor market and realize the free movement of labor and fair competition among regions. China’s labor market is still at an immature stage, and the employment situation is poor, and the structure is unbalanced. For encouraging labor mobility, it is more important to cancel various policies and regulations restricting labor mobility, such as the household registration system. The labor market in China is dispersed due to the country’s distinctive household registration system. When seeking for a job, there will be evident discrepancies if two equally exceptional individuals have different household registrations. The elimination of the household registration system will thus make labor mobility easier and increase social intergenerational economic mobility. In order to achieve the best possible integration between urban and rural regions, the government might set up an urban-rural integrated social security system and progressively implement an urban-rural integrated system. In addition, agriculture is connected to peoples’ means of subsistence. Although labor is encouraged to enter cities to work, it also needs to be effectively controlled. Too much labor cannot flow into cities, and the development of agriculture should not be neglected. Therefore, the government needs to focus more on the investment and construction of rural facilities to ensure that farmers’ incomes increase, increase their enthusiasm for production, increase jobs, and reduce the overall employment pressure in society.Third, it is necessary to ensure equal employment opportunities for men and women and reduce gender discrimination. Gender discrimination at work not only affects the sustainable development of society but also affects the stability of society. China should improve the legal protection system, strengthen the enforcement of laws, improve laws and regulations, and establish a fair recruitment system. Moreover, it is necessary to strengthen the executive power of the legislature, strive to establish an antidiscrimination law, and realize that there is a law to abide by, which truly guarantees social fairness and protects the rights and interests of workers from the fundamental source. In addition, the government can increase policy approval in the implementation of relevant legal measures and can also encourage enterprises to attract more women. At the same time, companies need to increase jobs suitable for women, identify themselves with independent women in today’s society, and actively assist in optimizing the industrial structure to promote employment and eliminate discrimination.


## 4. Conclusion

The disparity in opportunities that affect income distribution and the unequal allocation of opportunities that results are the primary issues in the area of income distribution today, not merely the apparent expansion of the income gap. This could be a significant contributing factor to the growing income divide. Therefore, it is crucial to examine not only the outcomes of income distribution but also the root causes of income disparity when examining the connected concerns of income distribution. Using big data technologies, this study examines the intergenerational economic mobility of Chinese urban and rural inhabitants and develops a matching model. The experimental investigation provides evidence that big data technologies may contribute significantly to the study of the intergenerational income of Chinese urban and rural inhabitants.



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*Source: 6230617-2022-10-03.xml*