Li, Pengpeng; (2025) The Effectiveness of China's Talent Housing Subsidies on Talent Agglomeration. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

The thesis builds on the phenomenon that skyrocketing housing prices in top cities drive some talented workers to leave as they cannot find affordable and suitable housing there. To retain talent, many municipalities have adopted housing subsidies for targeted talent, known as Talent Housing (TH) policies in China. To ensure sufficient incentives for different levels of talent, TH policies adopted a “gradient subsidy” approach, where subsidies increase with talent level. However, due to the complex effects (i.e., positive, negative, composite) of housing prices on talent flow, along with the impact of cities’ socio-economic conditions on talent flow, policymakers are uncertain about whether the implementation of TH policies will have an expected outcome and what role TH policies can play in influencing talent flow. Therefore, the research aim of this thesis is to explore the effectiveness of TH policies in attracting talent and forming agglomeration of talented human capital, considering both external determinants from “policy environment” (e.g., cities' socio-economic conditions) and internal determinants from “policy content” (e.g., subsidy allocation among different talent levels). It is because an effective public policy hinges not only on whether the policy content enhances the policy's efficiency and sustainability through justified means, but also on whether the policy environment provides an appropriate socio-economic context for its implementation. This principle also applies to TH policies. To unravel the mechanism by which TH subsidies influence talent mobility, a theoretical framework is constructed, grounded in Krugman's (1991) "New Economic Geography" theory and Roback's (1982) framework of individual utility maximization. Furthermore, to systematically investigate the effectiveness of TH policies, three research questions (RQs) are proposed. RQ1: Do TH subsidies, including rent subsidy and homebuying subsidy, effectively help to attract talented workforce and form the agglomeration of human capital across different Chinese cities? How do the socio-economic conditions of a city (e.g., work opportunity, housing affordability, city’s tier) influence the effectiveness of TH subsidies in this city? RQ2: Given the undermined effectiveness of TH subsidies in some lower-tier cities, what is the main determinant driving talent of these cities to “move up the urban hierarchy”? RQ3: Under the gradient subsidy, how do TH subsidies affect the housing burden and settlement intention of multi-level human capital (i.e., high-level talents, ordinary-level talents)? To begin with, to address RQ1, System Generalized Method of Moment (SYS-GMM) is applied to analyze the relationship between talented human capital and TH subsidies in 70 Chinese cities and whether a city’s tier affects the level of effectiveness of the TH policies. The period is from 2011 to 2020, when most selected cities start to introduce TH policies. The results show that TH subsidies, in particular homebuying subsidies, have a one-year lag positive effect on talent agglomeration in higher-tier cities. In contrast, the TH policies are less effective in lower-tier cities, most of which have low work opportunities and high housing affordability. These findings guide my new thinking about RQ2. Therefore, to address RQ2, I investigate the key determinants undermining TH’s effectiveness in lower-tier cities with Gravity Model and the dataset of China Migrants Dynamic Survey (CMDS). The dataset provides Origin-Destination (OD) data about migrant flow among these 70 cities. The results show that despite policymakers' efforts to provide competitive TH subsidies in lower-tier cities, talent outflow persists due to the factors driving talent outflow largely unaffected by these policies. Higher-tier cities have advantages in higher wages, better amenities, and economic connections that lower-tier cities often do not have. While lower-tier cities may have affordable housing prices and better natural amenities, these advantages do not sufficiently deter talent outflow. Finally, to address RQ3, I study the real impacts of TH policies on human capital of different levels, and a case study of Nanjing city is conducted to complement the empirical findings. The study surveys 600 respondents via questionnaires and 30 via interviews, to collect the opinions of high-level talents, ordinary-level talents, and non-talents on TH policies, utilizing Structural Equation Model to verify the policies' impact on housing burden and settlement intentions across different talent levels. The evaluation of TH subsidy's effectiveness in achieving its objectives reveals that it has a positive effect on the short-term goal (alleviating housing burden) to varying degrees, but its effect on the long-term goal (enhancing settlement intentions) is limited, showing a discernible gap from intermediary objectives to final objectives. In summary, these findings of three RQs indicate that the effectiveness of Talent Housing (TH) subsidies on talent agglomeration is complex and multifaceted. The effectiveness of TH subsidies varies based on factors such as the economic conditions of different cities and the specific policy objectives, whether they aim to alleviate housing burdens or enhance settlement intentions for talented individuals. These factors lead to divergent outcomes across cities. Both external and internal factors influence the outcomes, albeit in different ways. External factors, i.e., a city’s socio-economic conditions, act as direct constraints on TH effectiveness. On the one hand, cities with weaker economic foundations often face challenges in implementing TH policies effectively, creating a less favorable “policy environment” for achieving desired outcomes. On the other hand, internal factors, such as gradient subsidies with preference on higher level human capital, exert a more indirect influence, as these subsidies can spark debates over inclusiveness and, over time, may undermine effectiveness of TH policies in the long run. By addressing these research questions, the study makes contributions to the existing knowledge as follows: First, this study makes a theoretical contribution for advancing existing theories of talent migration. By incorporating subsidy incentives into the analytical framework, the study aims to overcome the weakness of existing talent migration models in effectively explaining the role of policy interventions on housing cost. Furthermore, the study refines existing talent migration models by reconceptualizing housing costs—from a static market constraint to a policy-malleable lever—demonstrating how targeted subsidies disrupt conventional location choice models that treat housing as a static input. Second, the study provides a workable policy evaluation model of TH subsidy. It generates quantitative empirical evidence on the effectiveness of TH subsidies in attracting talent, which is insufficiently explored in the current talent migration research. The study also represents the first attempt to quantify the amount of TH subsidies, to the best of my knowledge. Third, this study expands the limited empirical research on the effectiveness of China’s Talent Housing (TH) policies in attracting talent by focusing on two critical yet underexplored factors: intercity differences and gradient subsidies. By analyzing these determinants, the research provides valuable insights into how public housing policies can influence, drive, and guide talent mobility across cities, ultimately enhancing cities' competitiveness.

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