自改革开放以来，中国经历了较长时期的高速增长，经济和社会发展都有了质的飞跃，国民生活水平也得到极大提升。但是，中国社会收入差距大、发展不均衡不充分的问题依旧严峻，贫富分化问题日益凸显。公平正义是维系社会稳定的重要因素，也是国家和人民群众关注的焦点问题。代际流动性是衡量社会公平的重要方面。提升代际流动是促进机会公平，确保经济社会发展活力的必要条件。

       与此同时，全球新一轮科技进步的浪潮势不可挡，深化以自动化、数字化、信息化和智能化为核心的产业变革，是当今和未来全球产业发展的大趋势。产业智能化在影响经济格局的同时也对收入分配格局产生影响。因此，挖掘产业智能化和代际流动性的关系对于深入理解中国经济发展模式，促进中国经济向公平、共享、高效的高质量发展迈进，具有重要意义。国内外相关前沿文献更多关注产业智能化发展的经济增长效应和劳动就业及收入效应，相对忽视了产业智能化对社会公平，特别是对体现社会机会公平与长期动态公平的代际流动性的影响，且尚未理清产业智能化对就业和收入分配的作用机制，尤其是缺乏来自发展中国家的证据。

       基于此，本文从产业智能化视角分析中国代际流动性变动及其影响路径，构建包含理论机制、事实分析、实证检验、政策启示的全局分析框架。主要内容如下：

       第一，阐述本文的研究背景、研究问题、研究思路和内容、研究方法以及研究创新点。第二，通过系统梳理代际流动性和产业智能化的相关文献，构建两者相互关联的概念框架。第三，在综合运用现有代际流动性理论和有偏技术进步理论等相关理论的基础上，引入知识更新和跨代人力资本传递机制，推导出技能偏向型技术进步引发代际职业、收入、教育流动性变动的路径，为产业智能化的流动性效应提供模型化解释，奠定全文理论基础。第四，主要利用多个微观调查数据，对中国收入不平等和代际收入、职业、教育流动性的长期变动趋势及其城乡、性别、地区异质性进行全面刻画。第五，实证检验部分分别从收入、职业、教育三个维度检验了产业智能化对代际流动性的影响。利用地区-行业-个体三个层面的数据，主要采用工业机器人渗透度、人工智能专利、机器人进口等多个产业智能化指标，构建双向固定效应模型、Probit模型、工具变量模型、样本选择模型和分位数回归模型等多种计量模型，对产业智能化如何影响代际流动性进行效应分析、内生性检验、稳健性检验和异质性分析。此外，还检验了产业智能化通过影响劳动就业、收入和技能溢价作用于代际流动性的传导机制。最后，在总结全文的基础上，提出政策启示，并作进一步研究展望。

       本文得到以下主要研究结论：

     （1）智能化技术在生产任务中替代人类，对劳动力需求产生负向的替代效应；同时也产生积极的生产率效应和岗位创造效应，增加就业需求，补偿了替代效应，并推动就业结构转型，为代际流动性提升提供经济空间。首先，产业智能化具有通用技术进步特征，通过增加劳动力收入放松了低技能家庭人力资本投资预算约束，存在代际流动性提升效应。其次，产业智能化具有有偏技术进步特征：一方面，技术-技能互补扩大当前高技能劳动力需求和高技能岗位供给，加快知识更新，减少了跨代人力资本传递对职业和收入获得的重要性，更多强调个人能力和努力的重要性，也存在代际流动性提升效应；另一方面，技能溢价扩大高、低技能劳动力收入差距，引起对子代人力资本投资的不平等，存在降低未来代际流动性的可能。产业智能化的代际流动性总效应是所有效应共同作用的结果。

    （2）我国代际流动性向好发展。从调查年份看，2000—2020年以收入、教育、职业衡量的代际流动性呈现一定的上升趋势，从出生年代看，“60后”到“90后”出生子代的代际流动性也有所提高，虽然个别年份存在一定波动。这一趋势结果体现了随着中国经济社会的发展进步，劳动市场不断完善，国民的经济上升机会进一步扩大，代际流动性与机会公平得到改善。

    （3）实证检验结果显示，首先，产业智能化增加能显著降低代际收入弹性和代际收入秩关联系数，促进子代收入阶层向上流动并抑制向下流动；对低收入阶层、中等技能家庭以及东中部地区、教育水平较高、收入不平等较低地区的促进效果更大；在产业智能化程度较高的地区，公共教育支出和市场化发展对代际收入流动性发挥了更大的积极作用。同时，产业智能化刺激职业结构升级，降低子代与父代的职业地位相关性，进一步加强对代际收入流动性的促进作用。其次，产业智能化通过提高子代的人力资本积累提升代际教育流动性，即降低子代与父代受教育水平的相关性，促进教育向上流动，对中低技能群体、女性、城镇户籍人口、教育水平较低地区和教育水平分布差异较大地区的改善效果更大。最后，产业智能化通过产业升级效应推动劳动力转移，为社会提供更多的高技能劳动岗位，同时还提高了教育溢价和非常规性认知任务溢价，增加中高收入群体的规模，不会造成大量失业和收入下降。

       基于上述结论，本文主要提出以下几点政策启示：一是增加劳动力技能培训，提高劳动者终身学习能力。二是提高对低收入家庭教育的财政支持，促进地区间教育质量的均衡发展。三是有效推进智能化技术研发应用，促进地区间产业智能化均衡发展。四是完善市场经济体制，营造公平竞争环境。五是促进劳动要素自由流动，加强公共服务和社会保障。通过以上措施，以期促进我国智能经济发展的红利更公平惠及全体人民，从而有利于更好实现共同富裕。

      Since embarking on economic reform and opening up, China has witnessed a prolonged period of rapid economic growth, leading to a qualitative shift in economic and social development, thereby significantly improving living standards. Nevertheless, China grapples with persistent issues of income inequality and imbalanced development, prominently marked by a widening wealth gap. The imperative of fairness and justice in maintaining social stability underscores its importance as a central concern for both the state and its citizens. Intergenerational mobility, as a crucial dimension of social equality, plays a vital role in fostering equality of opportunity and ensuring the vitality of socioeconomic development.

       Concurrently, the ongoing global technological revolution, emphasizing industrial transformation through automation, informatization, digitization, and intelligence, is an unstoppable trend. This transformation not only reshapes economic structures but also profoundly influences income distribution patterns. Investigating the relationship between industrial intelligence and intergenerational mobility holds profound significance, offering deeper insights into China's economic development paradigm and steering the economy towards high-quality, fair, inclusive, and efficient progress. Existing literature predominantly focuses on the growth and labor employment effects of industrial intelligence, overlooking its impact on social equality, particularly intergenerational mobility—a critical reflection of both opportunity equality and long-term fairness. Understanding the mechanisms by which industrial intelligence influences employment, income distribution, and its effect on intergenerational mobility remains underexplored, with limited evidence from developing countries.

       This dissertation aims to explore the impacts of industrial intelligence on intergenerational mobility in China, establishing a systematic research framework encompassing theoretical mechanisms, empirical analyses, and policy implications. The main contents are as follows:

       Firstly, the dissertation introduces the research background, theme, structure, methodology, and potential contributions. Secondly, it reviews the literature on intergenerational mobility and industrial intelligence, constructing a conceptual framework that delineates interconnections between the two. Thirdly, the study combines the intergenerational mobility theory with the biased technological progress theory, and introduces the mechanisms of knowledge updating and household human capital transfer into the model to deduce the channel through which skill-biased technological progress affects intergenerational occupation, income, and education mobility, forming the theoretical bedrock for the subsequent analysis. Fourthly, utilizing various micro-survey data, the dissertation provides a comprehensive depiction of long-term trends in income inequality and intergenerational income, occupation, and education mobility in China, as well as their urban-rural, gender, and inter-regional variations. Fifthly, empirically examining the impact of industrial intelligence on intergenerational mobility from perspectives of income, occupation and education. The analysis uses data from the three-dimensional levels of region, industry and individual, and designs various industrial intelligence indicators such as penetration of industrial robots, AI patent and robot import. Econometric models, such as fixed-effects models, Probit models, instrumental variable models, sample selection models, and quantile regression models, are utilized for empirical analysis, including causal identification, robustness check, and heterogeneous testing. Additionally, the study explores the mechanisms through which industrial intelligence acts on intergenerational mobility by influencing labor employment, earnings, and skill premiums. Finally, drawing from the whole study, the dissertation presents feasible policy recommendations and explores future research possibilities.

       The main conclusions of this study are as follows:

       (1) Intelligent technology has negative substitution effects on labor demand by replacing human in production tasks. It also has positive compensatory effects such as productivity effects and new task creation effects which increase labor demand and drive structural transformations in employment, creating space for heightened intergenerational mobility. Industrial intelligence displays generic technological progress characteristics. It raises labor income and relaxes budget constraints on human capital investment for low-skilled households, promoting mobility. Simultaneously, it also displays biased technological progress characteristics. On one hand, technology-skill complementarity has expanded the demand for high-skilled labor and the supply of high-skilled positions, and accelerated knowledge updates, diminishing the importance of intergenerational human capital transmission in terms of occupation and income acquisition, emphasizing more on the significance of individual capability and effort. This enhances intergenerational mobility. On the other hand, skill premiums have widened the income gap between high and low skilled labor, leading to unequal human capital investments in offspring. This inequality potentially reduces intergenerational mobility in the future. The overall effect of industrial intelligence on mobility is a result of the combined influence of all these effects.

       (2) China's intergenerational mobility is on an upward trend. Intergenerational income, education, and occupational mobility have shown an overall increase from 2000 to 2020. Offspring born between the 1960s and the 1990s also demonstrate growing intergenerational mobility. Although there are fluctuations in some years/cohorts. The trends reflect that with China's economic and social development, the labor market has improved and economic opportunities have expanded, ultimately enhancing intergenerational mobility and opportunity equality.

       (3) Empirical results indicate that, firstly, regional industrial intelligence development significantly diminishes intergenerational income elasticity (IGE) and intergenerational income rank association (IRA), encouraging upward mobility and discouraging downward mobility for offspring. This positive effect is more pronounced for low-income groups, middle-skilled families, and regions in central and eastern provinces, and characterized by higher education and lower income inequality. Industrial intelligence also amplifies the positive impacts of public education expenditure and marketization on intergenerational income mobility. Moreover, it stimulates occupational structure upgrading, reducing the correlation between children's and parents' occupation statuses, further strengthening intergenerational income mobility. Secondly, Industrial intelligence also enhances the human capital accumulation of school-age children, reducing intergenerational education correlation and promoting upward mobility, especially for middle- and low-skilled classes, women, urban individuals, and regions with lower education and higher education inequality. Finally, by facilitating labor transfer through industrial upgrading and augmenting the education premium and non-routine cognitive task premium, industrial intelligence expands the returns for high-skilled labor and the size of the high-return group rather than resulting in significant unemployment or income reduction.

       Based on these conclusions, the dissertation proposes the following policy recommendations: (1) Enhance skills training for labor and cultivate their lifelong learning abilities. (2) Increase financial support for education in disadvantaged families and foster balanced development of education quality across regions. (3) Promote effective development and application of intelligent technology, facilitating the coordinated development of intelligent industries across regions. (4) refine the market economic system and establish an enabling environment conducive to safeguarding equality and justice. (5) Remove barriers to labor mobility and bolster public services and social security. Through these measures, the aim is to ensure that the achievement of intelligent economic development in China benefit all citizens more equitably, thus contributing to achieving shared prosperity more effectively.