We model censorship as a dynamic game between an agent and an evaluator. Two types of public news, good and bad news, are informative about the agent’s ability. However, the agent can hide bad news from the evaluator, at some cost, and will do so if and only if this secures her a significant increase in tenure. Thus, the evaluator faces a bandit problem with endogenous news processes. When bad news is conclusive, the agent always censors when the public belief is sufficiently high, but below a threshold, she entirely or partially stops censoring. The possibility of censorship hurts the evaluator and the good agent, and it may also hurt the bad agent. However, when bad news is inconclusive, we show that the good agent censors bad news more aggressively than the bad agent does. This improves the quality of public information and may benefit all players.

We introduce private learning into a banking model to study the dynamics of relationship lending. In our model, an entrepreneur chooses between bank and market financing. Bank lending facilitates learning over time, but it subjects the borrower to the downside of information monopoly. We construct an equilibrium in which the entrepreneur starts with bank financing and subsequently refinances with the market, and we find conditions under which this equilibrium is unique. Our model generates several novel results. First, both information asymmetry and entrepreneur's reputation are accumulated over time. As a result, the bank will roll over bad loans for entrepreneurs who have accumulated enough reputation for the prospect of future loan sales. Second, this incentive to extend and pretend gets mitigated when the entrepreneur faces financial constraint. We further endogenize learning as bank's costly decision and show how it is affected by asymmetric information and financial constraint.

This paper analyses under-adoption of sanitation and the effectiveness of loans and price subsidy policies to increase coverage in the developing world. Under-adoption may be the result of externalities and borrowing constraints: while sanitation is an expensive investment for many poor potentially liquidity constrained households, it also generates positive health externalities. To investigate the impact of the two policies on sanitation coverage I estimate a dynamic model of household demand on a unique dataset from rural India. The model embeds both sources of market failure in order to compute equilibrium adoption levels under the loan and the subsidy policy. Using simulations from the estimated model, I study optimal policy design in an equilibrium setting with potential multiple equilibria. Counterfactual analysis reveals that existing sanitation levels are on average 53% below the social optimum, implying under-adoption. I find price subsidies to be more cost effective at increasing sanitation coverage. However, the policy impacts are heterogeneous by coverage levels: in villages with low coverage loans are equally, if not marginally more, effective. A price subsidy has a high social rate of return where externalities account for a substantial fraction of the policy impact. While a sanitation loan policy generates smaller social returns it is also cost efficient under targeted delivery.