We construct a daily Chinese Housing Market Sentiment Index by applying GPT-4o to Chinese news articles. Our method outperforms traditional models in several validation tests, including a test based on a suite of machine learning models. Applying this index to household-level data, we find that after monetary easing, an important group of homebuyers (who have a college degree and are aged between 30 and 50) in cities with more optimistic housing sentiment have lower responses in non-housing consumption, whereas for homebuyers in other age-education groups, such a pattern does not exist. This suggests that current monetary easing might be more effective in boosting non-housing consumption than in the past for China due to weaker crowding-out effects from pessimistic housing sentiment. The paper also highlights the need for complementary structural reforms to enhance monetary policy transmission in China, a lesson relevant for other similar countries. Methodologically, it offers a tool for monitoring housing sentiment and lays out some principles for applying generative AI models, adaptable to other studies globally.

This paper provides evidence on the cost of political interference on banks with Chinese Private Enterprise Survey data between 2002 and 2012. Using regional political turnovers as a proxy for political influence, we show that political motivations for future promotions distort the bank lending decisions and crowd out lending to private firms. Besides, firms with business connections are more sensitive to turnover, while political connections are not significantly affected. These lending distortions are more considerable where competition for future promotion is more intense and where incumbents have more influence over banks. Moreover, the effect is especially pronounced for small firms. As a result of reduced bank credit, firms’ total credit availability decreases and they have to cut investments. Overall, our results suggest that preferential lending to politically important sectors has negative spillovers and can lead to costly crowding-out of private sectors.

From the perspective of corporate labor employment, we examine whether debt pressure on local governments prompts them to shift part of their social responsibilities to local firms. We conduct an analysis on Chinese local government debt (LGD) data and find that when LGD is higher, local firms are less likely to cut labor costs when their sales decrease, indicating greater labor cost stickiness. We attribute this to the responsibility-shifting effect, i.e., with heavier debt burdens, local governments intervene more in corporate labor decisions by restricting employee layoffs. Consistent with this argument, we find that the effect of LGD on labor cost stickiness is more pronounced for state-owned and politically connected firms; in regions with lower marketization levels and government fiscal self-sufficient capacities; and when regional unemployment rates, macroeconomic uncertainty, and political risk are higher. We show that through responsibilityshiftingamid high LGD, local governments benefit from a reduction in social expenditures. However, firms with stickier current labor costs will have lower subsequent productivity and market value, despite local governments reciprocating with more subsidies. Overall, LGD not only adversely impacts firm financing through the crowding-out effect but also erodes firm value through the responsibility-shifting effect.