This paper studies the effects of social learning on the transmission of Covid-19 in a network model. We calibrate our model to detailed data for Cape Town, South Africa and show that the inclusion of social learning improves the prediction of excess fatalities, reducing the best-fit squared difference from 20.06 to 11.28. The inclusion of social learning both flattens and shortens the curves for infections, hospitalizations, and excess fatalities. This result is qualitatively different from flattening the curve by reducing transmission probability through non-pharmaceutical interventions. While social learning reduces infections, this alone is not sufficient to curb the spread of the virus because learning is slower than the disease spreads. We use our model to study the efficacy of different vaccination strategies and find that a risk-based vaccination strategy–vaccinating vulnerable groups first–leads to a 50% reduction in fatalities and 5% increase in total infections compared to a random order benchmark. By contrast, using a contact-based vaccination strategy reduces infections by 9% but results in 64% more fatalities relative to the benchmark.

We incorporate a model of frictions into the bunching-based elasticity estimator to rationalize diffuse bunching around kinks and mass above notches in empirical distributions. Model agents draw a sparse set of opportunities from a Poisson process, approximating a broad class of frictions including search costs, inattention, and lumpy adjustment; the predicted density depends on the standard structural elasticity and a money-metric “lumpiness parameter.” We estimate the model using administrative tax data on South African small-businesses, recovering moderate elasticities of taxable income between 0.2 and 0.3 at higher incomes, and larger elasticities at low incomes. Firms appear to treat the bottomkink as a notch, and firms with paid tax practitioners exhibit sharper bunching, driven primarily by lower frictions rather than a higher elasticity.

Emerging markets are characterized by frequent periods of large and unexpected exchange rate depreciations. These events create opportunities for foreign investors to purchase domestic assets at a discount, especially if these assets have sticky prices. We show this to be the case in the housing market in Cape Town, South Africa. Using property transaction data, I find that foreign non-residents buy more properties following large exchange rate depreciations—in the lower quartile of month-on-month changes. I find no evidence of a similar effect for other buyers, suggestive of strong exchange rate-related motives. Using these depreciations as demand shocks to foreign non-resident buyers, I find that this increased demand leads to an increase in house prices of 3.39%. I find that foreign non-resident buyers pay 10.42% more than other buyers for otherwise identical properties and that this tendency to pay a premium accounts for around 27% of the observed causal impact of foreign demand on house prices.