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.

By using a unique proprietary dataset and implementing the Wuhan (China) lockdown from January to April 2020 as a natural experiment, we find that individual mutual fund investors in Wuhan significantly reduced their trading frequency, total investment of their portfolios, and risk level of their invested funds during the lockdown period as compared to investors in other cities. These changes are stronger for older investors and are reversed soon after the lifting of the lockdown. Our results suggest that the elimination of face-to-face interaction among individual investors reduced their information sharing, which led to more conservatism in their financial trading. These results are not supported by the alternative explanations of limited investor attention and temporary changes in personal circumstances, including depression and/or income reduction, during the lockdown period. Finally, consistent with the theory of naïve investor trading, we also find that investors received higher trading returns during the lockdown.

Using administrative universal business registration data as well as primary offline and online surveys of small businesses (including unregistered self-employments) in China, we examine (i) whether digitization helps small and medium enterprises (SMEs) better cope with the COVID-19 pandemic, and (ii) whether the pandemic has spurred digital technology adoption. We document significant economic benefits of digitization in increasing SMEs' resilience against such a large shock, as seen through mitigated demand decline, sustainable cash flow, ability to quickly reopen, and positive outlook for growth. Post the January 2020 lockdown, firm entries exhibited a V-shaped pattern, with entries of e-commerce firms experiencing a less pronounced immediate drop and a quicker rebound. Moreover, the pandemic has accelerated the digital transformation of existing firms and the industry in multiple dimensions (e.g., altering operation scope to include e-commerce, allowing remote work, and adopting electronic information systems). The effect persists more than one year after reopening, and is more pronounced for certain sectors, firms in industrial clusters, and areas with more digital inclusion but less financial efficiency, constituting initial evidence for the long-term impact of the pandemic and the supposedly transitory mitigation policies.

We study the income inequality among streamers using the administrative data of a leading Chinese live-streaming platform. The live-streaming technology enables a superstar to produce new entertainment products matched with demand and occupies a larger market share. Imagine an extreme case; the best streamer hosts live for 24 hours, earns all possible income, and leaves zero time for other streamers. Our data show that the income distribution of the highest-paid streamers follows Zipf’s Law and appears to be even more concentrated than any offline business: NBA top players, Forbes celebrities, and billionaires. Income inequality increased rapidly as the platform expanded from 2018 to 2020 — for example, the income share of the platform’s top 10 streamers increased from 14.82% to 45.15% as its revenue grew by 142%. To estimate inequality elasticity to the market size, we study four quasi-experimental shocks: potential market size proxied by economic development and Fintech coverage, quarter-end revenue spikes induced by the seasonal incentive regime, user surge induced by capital raising, and the Covid-19 lockdown in Wuhan. Gini coefficient elasticity ranges fromm1.3% to 10.6% estimated from the cross-city variations (local economic development and Covid-19 Wuhan lockdown); the time-series variations (quarter-end and user surge before capital raising) imply an elasticity ranging from 3.6% to 25.5%.

Exploiting staggered COVID-19 lockdowns and reopening across different regions in China, we study how lockdowns affect the investment decisions of venture capital (VC) investors and whether such changes are temporary or enduring in the post-pandemic era. Contrary to the conventional wisdom that lockdowns exacerbate the “tyranny of distance” (i.e., VCs avoid investing in remote ventures), our findings suggest the “death of distance”: VCs invest in remoter ventures during a lockdown and such effects persist even after the economy reopens. Such lockdown effects are more pronounced when there is better internet infrastructure, when the level of information asymmetry between VCs and entrepreneurs is lower, and when VCs are more experienced. The lockdown effects can be explained by the advancement and adoption of remote communication technology as a response to the social distancing requirements. As geographic boundaries of VC investment are shattered by remote communication technology, local competition among VCs has been intensified, the monopoly power of VCs has been curtailed, and the regional inequality of entrepreneurial access to VC financing has been mitigated.

Although big data technologies such as digital contact tracing and health certification apps have been widely used to combat the COVID-19 pandemic, little empirical evidence regarding their effectiveness is available. This paper studies the economic and public health effects of the "Health Code" app in China. By exploiting the staggered implementation of this technology across 322 Chinese cities, I find that this big data technology significantly reduced virus transmission and facilitated economic recovery during the pandemic. A macroeconomic Susceptible-Infectious-Recovered (SIR) model calibrated to the micro-level estimates shows that the technology reduced the economic loss by 0.5% of GDP and saved more than 200,000 lives by alleviating informational frictions during the COVID-19 outbreak.