We study how U.S. dollar fluctuations transmit through domestic supply chains in emerging markets. Large firms borrow in foreign currency and extend trade credit to domestic partners, exposing the supply chain to exchange rate risk. We develop a model where financially constrained suppliers pass through shocks to buyers, while unconstrained firms absorb them. Using quarterly firm-level data from 19 emerging markets, we provide empirical evidence consistent with the model’s predictions. We find that even highly exposed firms reduce trade credit only modestly following a depreciation, while accepting large profit losses, suggesting that firm-to-firm credit relationships partially shield downstream firms from financial shocks.

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.

Policy uncertainty can undermine the power of government subsidies to stimulate environmentally friendly research and development. We show that Chinese firms’ green R&D falls as the uncertainty of environmental subsidies rises: Exogenous, weather-driven air pollution variability induces subsidies to fluctuate, and firms in areas with high weather-driven subsidy variability undertake less green R&D and hire fewer technical employees, controlling for the average level of subsidies. Heavy emitters and environmental technology firms are more affected. The results also illustrate how policy uncertainty can arise when policymakers are influenced by conditions that are salient but with causes that are difficult to disentangle.

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.

We test whether forecast bias affects household stock trading by combining measures of bias elicited in laboratory experiments with administrative trade-level data. On average, subjects exhibit positive forecast bias (i.e., extrapolators), while a large minority exhibit negative forecast bias (i.e., contrarians). Forecast bias is positively associated with past excess returns of stocks that are purchased: Extrapolators (contrarians) purchase past winners (losers). Forecast bias is negatively associated with the capital gains of stocks that are sold. Furthermore, forecast bias explains investor heterogeneity in the relation between market returns and net flows to stocks. Overall, our study provides evidence of a common mechanism – forecast bias – that links past returns to trading decisions for purchases, sales, and net flows.

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 study the nature and effects of cultural biases in choice under risk and uncertainty by comparing peer-to-peer loans the same individuals ( lenders ) make alone and after observing robo-advised suggestions. When unassisted, lenders are more likely to choose co-ethnic borrowers, facing 8% higher defaults and 7.3pp lower returns. Robo-advising does not affect diversification but reduces lending to high-risk co-ethnic borrowers. Lenders in locations with high inter-ethnic animus drive the results, even when borrowers reside elsewhere. Biased beliefs explain these results better than a conscious taste for discrimination: lenders rarely override robo-advised matches to ethnicities they discriminated against when unassisted.

Disagreement about macroeconomic fundamentals accounts for only part of the disagreement about future interest rates, creating a “disagreement correlation” puzzle. This puzzle arises because standard equilibrium models with belief differences predict a strong link between asset return disagreement and fundamental disagreement, a link not supported by the data. We address this puzzle by introducing a model where disagreement about future demand for savings—driven by disagreement over the prevalence of patient versus impatient investors in the economy—generates asset return disagreement. Our mechanism produces stochastic yield volatility, time-varying bond risk premia, and an upward-sloping yield curve. Empirically, we construct a proxy for demand disagreement by isolating the component of yield disagreement unrelated to disagreement about macro-fundamentals. This proxy is positively related to yields and their volatilities, and predicts future bond risk premia, consistent with the predictions of our demand disagreement model.