Knowledge that Transforms

This paper investigates how cashless payment affects credit access for underserved populations using data from Alipay, a leading Chinese BigTech platform with over 1 billion users that offers a wide range of financial services. By exploiting the staggered rollout of Alipay-bundled shared bikes across cities as a natural experiment and analyzing a representative Alipay user sample, I find that cashless payment adoption increases credit access by 56.3% and that a 1% rise in payment flow increases credit lines by 0.41%. These effects are stronger for less educated and older individuals, who have traditionally faced greater barriers to accessing financial services.

I study how the cognitive demands of financial planning shape household decisionmaking with respect to consumption out of windfall income shocks. I build a quantitative model of bounded rationality in which reoptimization is costly. Households respond to windfall income shocks by choosing a finite planning horizon over which to reoptimize, and the optimal planning horizon is increasing in wealth and the magnitude of the income shock. Calibrated to U.S. data, the model’s distribution of consumption responses is consistent with three key facts: even highly liquid households have large consumption responses out of income shocks, the fraction of households with positive consumption responses increases with shock size, and conditional on responding, larger shocks generate smaller consumption responses.

Using mutual fund flows, we evaluate prospect theory with choice outcomes in the market. We provide strong support for prospect theory: under a standard set of parameters, funds whose past returns generate higher prospect theory value attract significantly larger future flows; we also find corroborative evidence using account-level data. Taking a revealed preference approach, we estimate the prospect theory parameters through a discrete choice model and find that our field-based estimates align well with previous experiment-based estimates. Moreover, we show that prospect theory offers a new framework for understanding flows, as it has explanatory power beyond existing drivers.

We use newspapers to create Equity Market Volatility (EMV) trackers at daily and monthly frequencies. Our headline EMV tracker moves closely with the VIX and the S&P500 returns volatility in and out of sample. We exploit the volume of newspaper text to construct forty category-specific EMV trackers. News about commodity markets, interest rates, real estate markets, aggregate activity, and inflation figure prominently in EMV articles. Policy news is another major source of market volatility: 30 % of EMV articles discuss tax policy, 30 % discuss monetary policy, and 25 % refer to some form of regulation. Combining our newspaper-based trackers with textual analysis of 10-K filings, we obtain monthly firm-level risk exposure measures. These measures help explain the cross-sectional structure of realized volatilities and its evolution over time, even after conditioning on firm and time fixed effects.