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.

Our investigation reveals that the top 10 shareholders are the only credible contenders for dominant control rights in China's listed corporations. To measure the ultimate control of these entities, we adopt the Shapley-Shubik power index and calculate the principal shareholder's control at the top of the control pyramid. Our results demonstrate that approximately 70% of firms exhibit an ultimate control value of 1. Additionally, our analysis reveals a non-linear relationship between the ultimate control, the tunneling behavior of the ultimate controller, and the executives’excess perk consumption .Specifically, our findings suggest that this relationship is characterized by a phase transition.

This research employs the Alkire-Foster approach to measure multidimensional poverty between 2012 and 2020 in China, followed by examining the role of the three-pillar pension system in mitigating household multidimensional poverty. With the China Family Panel Studies data, our measurement uncovers the sustainable effects and mechanisms of household participation in the multi-pillar pension system on poverty mitigation. The results indicate that more participation in the pension system mitigates the probability of being trapped in multidimensional poverty. The findings reveal the significance of state social insurance, enterprise annuity, and individual commercial insurance. The mitigation effect of market-oriented pillars is achieved through more investment in and consumption for livelihood assets. Based upon the sustainable livelihoods framework, livelihood assets ameliorate household capabilities in human, natural, financial, and psychological capital against risks, shocks, and uncertainties. Our research contributes to the knowledge of how household participation in pension pillars sustainably mitigates multidimensional poverty through micro-level mechanisms and to the policy praxis of why a facilitating state is called for poverty mitigation from the perspective of new structural economics.

Testing the household Euler equation with consumption data faces econometric challenges caused by large measurement errors in the data and a short time span. We adopt a framework to test the Euler equation with stock market data to alleviate the measurement error and short time span issues. Utilizing a data-driven group panel data method, we identify a heterogeneous pattern of Euler equation failure among different groups of listed firms. The identified degree of Euler equation failure is significantly related to firm characteristics that are associated with famous stock anomalies. We show that the correlations between the degree of Euler equation failure and firm characteristics provide a new set of stylized facts that can help us distinguish between different economic theories on Euler equation failures and asset pricing anomalies, and identify challenges facing current theories.

This study investigates the effect of a large-scale Internet upgrade program in China on the consumption of rural households. Using data from the China Family Panel Studies, we find that the Internet upgrade at the village level significantly increases rural households’ expenditures on total, online, and ofline consumption. Moreover, high-educated and young households, as well as those living in difficult-to-access villages exhibit a larger boost in total consumption. The mechanism analysis rules out income as the possible channel but highlights the role of online information exchange. ln particular, the variations in the increase in online time among households align with the heterogeneous responses in total consumption.

This paper represents the first attempt to show how heterogeneous shock experiences help explain the enduring scars on household future behaviors. Using a large-scale household survey with 15,652 observations combined with geospatial transportation big data, we identify a novel belief-updating mechanism through which crises may exert prolonged impacts on household asset allocation and consumption patterns. An increase in the duration of previous lockdown experience is associated with a 10.52% escalation in enhanced anxiety for future precautionary saving motivations. This experience-based learning perspective supports the resolution of long-lasting overreactions to negative shocks via belief revisions and extends to households’ consumption behaviors. The lingering effects continue to skew households' beliefs even when conditions improve. Additionally, households with different individual-based shock experiences may exhibit varying perceptions of external shocks, resulting in disparate belief revision processes.

How does information sharing affect consumers' usage of FinTech credit? Using a unique dataset of ``Buy Now, Pay Later (BNPL)" users from a large digital platform and exploiting a credit reporting policy change, we document that consumers significantly reduce their usage of BNPL credit when the BNPL lender becomes subject to credit reporting regulation. This reduction is more pronounced among borrowers with previous default records, who also become more disciplined in repayment behaviors, than those without such records. The decrease in BNPL usage also leads to a reduction in online consumption, supporting the financial constraint hypothesis. Our results suggest that information sharing can help alleviate overborrowing and overspending, with stronger effects observed among younger borrowers, those who previously consumed more, or those with credit cards. We also highlight the synergies between BNPL lending and Big Tech platforms' ecosystems, which imperfectly substitute for formal enforcement institutions.

Using iterative experiments to uncover causal links between critical policy details and outcomes helps to optimize policy design. This paper studies a large-scale staged fiscal stimulus program conducted during the COVID-19 pandemic, in which a provincial government in China disbursed digital coupons to 8.4 million individual accounts in consecutive waves and updated the program design each time. We find that ruling out unproductive program features leads to a pattern of increasing treatment effects over the waves and that program design matters more than the size of the fiscal stimulus in boosting spending. Our results show that (i) general coupons with no constraints on where the vouchers can be redeemed are more effective than specialized coupons in stimulating consumption in the targeted sectors; (ii) coupon packets with fewer denominations and shorter redemption windows tend to be more effective; and (iii) low-income residents and non-local residents are equally or even more responsive to the coupon program than other groups. Our results illustrate that generating variations in iterative policy experiments, combined with a timely assessment of individuals’ responses to marginal incentives, optimizes program design.

We develop novel high-frequency indices that measure climate attention, covering a wide range of both developed and emerging economies. This is achieved by analyzing the text of over 23 million tweets published by leading national newspapers on Twitter during the period from 2014 to 2022. Our findings reveal that a country experiencing more severe climate news shocks tends to see both an inflow of capital and an appreciation of its currency. In addition, brown stocks in highly exposed countries experience large and persistent negative returns after a global climate news shock. These outcomes align with the predictions of a risk-sharing model in which investors price climate news shocks and trade consumption and investment goods in global markets

In the context of promoting high-quality economic development, using digital technology to empower industrial transformation and upgrading, thus driving consumption growth has become a key problem that needs to be solved urgently. By using data at the prefecture-level cities in China from 2011 to 2020, the paper has discussed the influence of the digital economy on residents' consumption and its internal mechanism. Theoretical analysis and empirical test results have shown that first of all, the digital economy has significantly improved residents' consumption, and this conclusion is still valid after the endogenous test and robustness test. Secondly, mechanism analysis has shown that the digital economy can increase residents' consumption by promoting the upgrading of the industrial structure. Thirdly, the promotion effect of the digital economy on residents' consumption is heterogeneous between urban and rural areas and between different regions. Compared with urban, and eastern and central regions, the digital economy has a more significant incentive for residents' consumption in rural areas and western regions, indicating that its development is beneficial to narrowing the gap of consumption between urban and rural areas and between regions. Finally, the improvement effect of the digital economy on residents' consumption has marginal increment nonlinear characteristics, which is continuously strengthened with the upgrading of industrial structure. The above research conclusions can provide a theoretical basis for further improving the infrastructure of the digital economy, accelerating the integration of the digital economy with traditional industries, and building a consumer Internet.