劳动力的人力资本价值在现代经济活动中发挥重要作用。随着我国居民受教育水平的逐年提高，平均人力资本水平也在显著提升。这种提升为经济发展和经济结构转型注入了强劲动力。个人的人力资本水平主要通过学习得到增强。学生时期结束时的人力资本水平成为初始工作收入的主要决定因素。然而，我国一些地区存在的不平等现象导致了个体背景和教育成果的不同，这些不同也是个体间产生收入差异的重要原因。个体背景由个人特征、教育背景和家庭背景构成，这些组成因素共同影响着个人的人力资本水平及其收入水平。

除了学校，家庭是学生教育阶段的重要成长环境。第2章整理相关文献说明研究家庭背景与教育投入决策对人力资本形成的影响有着重要意义。本文从家庭视角构建了人力资本积累的理论模型，解释了家庭教育投入变化如何影响个人成长，并且讨论了异质性能力的形成。研究描述了父母在家庭消费和教育投资之间如何进行动态决策，具体阐释了家庭中不同教育投入塑造个体成长价值的过程以及对代际传递的影响。

第3章和第4章建立了人力资本模型分析了家庭教育决策特点，得到了不同成长阶段家庭的教育投入分配比例。利用边际分析说明了父母对教育的偏好、家庭消费以及时间分配对子女人力资本积累过程中教育投入水平产生不同程度的影响。而人力资本水平积累的差异能够解释一部分收入差异。

第3章的研究结论表明子女的初始人力资本水平越低，家庭教育投资在孩子成长早期的占比会越高。如果增加可用的投入水平，那么成长早期获得的投资比例会高于原有比例水平。同时，与子代人力资本水平成比例的未来转移支付会隐性的提升家庭总预算，理性的决策结果不仅提升了家庭的消费水平，还增加了教育投资，实现了子代更高的人力资本结果。第4章将异质性加入模型中，研究结论显示在家庭教育中，父母对子女教育的时间投入对早期子女成长具有非常高的价值。这是良性代际遗传成长实现家庭长期繁荣的关键方式。异质性能力的分析说明父母会向价值较高的能力投入更多，并且任何有价值的能力都值得被投资。如果父母过度期待孩子的某个能力，会出现教育投资过度的现象并无法使子女获得最好的成长。

第5章在三期模型中增加互联网学习部分与父母的闲暇效用进行分析。研究表明，如果投入要素是可替代的，互联网学习会提升人力资本水平同时抑制对其它教育投入的需求。如果投入要素是互补的，那么互联网学习会增加其它投入，并实现更高的人力资本水平。分析说明互联网教育具有多重影响，不仅提升子女的人力资本，同时多方面影响代际间的联系。第5章后半部分的创造性研究从效率提升、价值提升与活动创新三个角度说明了教育技术环境的进步改变了学习任务结构与投资结构。边际分析结论说明了学习活动发生变化时，决策者需要考虑实现人力资本水平的成本收益，低效率的新学习活动不应被过早应用。

实证分析使用2016年、2018年和2020年的中国家庭追踪调查(CFPS)的调查数据对影响人力资本水平的个体背景因素与家庭教育投入进行研究。第6章使用Mincer工资方程结构验证了受教育水平与其它背景变量对收入水平的影响。全样本回归中，教育的回报率为6.8%。通过内生性分析和稳健性检验说明了个人的受教育水平是产生收入差异的一个显著因素。异质性分析发现不同背景的样本得到的教育回报率存在显著差异，收入越高的家庭的个人教育回报率也越高，因此，缩小个人的教育水平差距也是减少收入差异的一个工具。

第7章研究了3个关于人力资本成长的主题。第一部分考察了个人特征与家庭背景对上学子女学习成绩的影响。结果表明，具有积极的背景因素有利于个人取得好成绩，同时个人努力是抵消激烈竞争导致相对成绩下降的关键因素。家庭中父母的关心交流会显著提高子女的学习成绩。异质性结果指出，家庭对低年级的教育支出显著提高子女学科成绩，说明了早期投资拥有较高的价值。子女成长过程中，父母的时间投入十分重要，家庭需要根据子女成长阶段目标选择合适的投入结构。

第二部分分析了教育支出与家庭消费、教育期望之间的关系。家庭非教育支出的消费增长1%，个人获得的教育支出增长约0.2%。教育期望的受教育年限增加1年，家庭教育支出增长2.2%。异质性结果显示，家庭背景差异会影响教育支出比重，低收入家庭消费支出比重更高，产生家庭消费需求对教育投入挤出的现象。结合第3章，政府对低收入家庭的转移支付政策可以有效的提高教育支出，同时需要帮助低收入家庭树立正确的教育支出观念，增强家庭中居民的受教育意愿，才能更好的激励低收入家庭增加教育投资水平，防止教育补贴被家庭内部决策转化为家庭消费。

第三部分检验了互联网活动对子女学习成绩的影响。结果显示，高频的互联网学习对成绩有显著的促进作用，而高频的互联网娱乐活动对成绩的负面影响并不显著。目前，互联网教育的进步不能完全改变传统教育过程，父母应该合理监督引导子女使用互联网进行学习获得提升。

The human capital value of labor plays a significant role in modern economic activities. With the annual improvement of educational levels among residents in our country, the average human capital level has also increased significantly. Thereby, the promotion injects strong momentum into economic development and structural transformation. An individual's human capital level is primarily enhanced through learning. At the end of the schooldays, the human capital level becomes the main determinant of initial work income. However, inequality in some regions of our country leads to differences in individual backgrounds and educational outcomes, which is also an important reason for the income disparity among individuals. An individual's background consists of personal characteristics, educational background, and family context, all of which collectively affect their human capital and income levels.

Besides school, family is an important growth environment for students during their education stage. Chapter 2 organizes relevant literature to elucidate the significance of studying the impact of family background and educational investment decisions on human capital formation. This paper constructs a theoretical model of human capital accumulation from a family perspective, explaining how changes in family education investments affect personal growth and discussing the formation of heterogeneous abilities. The study describes how parents make dynamic decisions between household consumption and educational investment, specifically illustrating the process by which different educational inputs within the family shape the individual's growth value and the implications for intergenerational transmission.

Chapters 3 and 4 establish a human capital model that analyzes the distinctive features of residents' educational decisions and calculates the distribution ratio of family education investments across different growth stages. Using marginal analysis, it illustrates how parents' education preferences, consumption, and time allocation influence the educational investment level during the growth of children's human capital. However, differences in the accumulation of human capital levels can explain part of the income differences.

Chapter 3 indicates that the lower the initial level of a child's human capital, the higher the proportion of family education investment during the early stages of the child's development. Moreover, an increase in the total investment budget leads to a higher proportion of early investment compared to the original allocation.    Concurrently, the transfer payments that children may provide in the future implicitly increase the family's total budget. Rational decision-making not only enhances the family's consumption level but also increases educational investments, achieving higher human capital outcomes. Chapter 4 incorporates heterogeneity into the model, and the research conclusions show that in family education, the time investment parents devote to their children's education is of very high value for the early development of the children. This is a key way for benign intergenerational growth to achieve long-term family prosperity. In the analysis of heterogeneous abilities, parents invest more in abilities with higher value to realize the concept of teaching according to individual aptitudes, and any valuable ability merits investment. However, if parents have excessive expectations for a particular ability of their child, it can lead to overinvestment and failure to foster optimal growth.

Chapter 5 incorporates Internet learning and parents' leisure utility into a three-period model for analysis. If input factors are substitutable, new investments will boost human capital while curbing the demand for other inputs. Conversely, if the input factors are complementary, Internet learning will increase the demand for other inputs and achieve higher levels of human capital. The model analysis suggests that Internet education, with its multiple impacts, not only enhances the accumulation of children's human capital but also exerts diverse influences on intergenerational connections. The latter segment of Chapter 5 presents innovative research that illustrates how advancements in educational technology have altered task structures and investment patterns from three perspectives: efficiency improvement, value enhancement, and activity innovation. Marginal analysis indicates that changes in study activities should consider the cost-benefit of growth, and inefficient new activities should not be prematurely applied.

The empirical analysis draws upon survey data from the China Family Panel Survey (CFPS) conducted in 2016, 2018, and 2020 to examine the personal background factors and household educational investments that influence the human capital level. Chapter 6 employs the Mincer wage equation framework to ascertain the impact of personal background variables and family environment variables on income. In the regression analysis encompassing the full sample, the return on education is estimated to range between 6.8% The endogeneity analysis and the robustness test show that the education level of individuals is a significant factor in the income difference. Furthermore, it is revealed that individuals from diverse backgrounds exhibit varying levels of returns to education. Families with higher incomes also have a higher rate of return on individual education. Therefore, narrowing the gap in the level of education of individuals is also a tool to reduce differences in earnings.

Chapter 7 examines three themes related to the growth of human capital. The first section investigates the impact of individual characteristics and family background on the academic performance of school-going children. The results indicate that positive background factors contribute to good grades, but personal effort remains a key factor in countering the relative decline in grades caused by intense competition. Parental involvement and communication within the family significantly improve children's academic performance. Heterogeneity outcomes suggest that family expenditure on education for lower grades significantly enhances children's academic performance, illustrating the value of early investment. As children grow up, parents' time investment is very important. Families need to choose the appropriate input structure according to their children's growth stage goals.

The second section analyzes the relationship between educational expenditure family consumption and educational expectations. A 1% increase in non-educational family consumption leads to approximately a 0.2% increase in individual educational expenditure. An additional year of desired education increases family educational expenditure by 2.2%. Heterogeneity results show that differences in family background affect the proportion of educational expenditure, with lower-income families having a higher proportion of consumption expenditure, indicating the crowding-out phenomenon of family consumption needs on educational investment. Combined with the conclusions of Chapter 3, government transfer payment policies to families can effectively increase educational expenditure, while also helping low-income families establish correct concepts of educational expenditure to better motivate them to increase their level of educational investment. This can enhance the willingness of residents in the family to receive education. The government should prevent educational subsidies from diverting to household consumption.

The third section tests the impact of Internet activities on children's academic performance. The results show that frequent Internet learning significantly promotes performance, while the negative impact of frequent Internet entertainment activities on performance is not significant. Currently, the advancement of the Internet will not completely change the traditional educational process, and parents should reasonably supervise and guide their children's use of the Internet for learning.