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I analyze how digital platforms, combined with mechanism-design theory, can be used to implement financial transactions and multi-period state- and history-contingent contracts including payments, credit, and/or insurance. I define and model the algorithmic (code-based) tools and mechanism-design constructs used in the implementation: digital escrow, enabling payment channels; multi-signature scripts, preventing unauthorized fund use by a single party; timelocks, restricting the spending or transfer of digital assets before a set time; and token accounts, used to track history-recording variables such as promised utility. Integrating mechanism-design solutions into the platform can deal with challenges such as the users’ participation, lack of trust, misrepresentation of true circumstances, and payment defaults and ensures that relevant asymmetric information or commitment obstacles are appropriately addressed so that stipulated payments are incentive-compatible and user reports are truthful. I present possible applications: an escrow-secured loan contract and a risk-sharing digital platform where users pay insurance premia or receive payouts based on reported income history.

I analyze the role of prepayment in a dynamic risk-sharing setting with information and/or commitment frictions. An insurance platform contracts with a risk-averse agent with stochastic income. Part of the income can be withheld in escrow as a prepayment. I consider three endogenously incomplete markets settings with different obstacles to risk sharing: limited commitment, private information due to hidden income, and both. I show that prepayment alleviates the limited commitment problem and improves insurance, including possibly to full insurance depending on the model parameters; however, prepayment is ineffective in the private information settings. In the setting with both frictions, I show that private information is the binding constraint.

We analyze how digital technologies can be used to improve safety nets for insurance against idiosyncratic and aggregate income risks, tailored to deal specifically with common market frictions: limited commitment, private information, and transaction costs. We illustrate the gains from incentive-compatible risk-sharing schemes for groups of economic agents such as Thai households or Spanish firms. We assess the best-fitting financial regime for each group and quantify large welfare gains from improved insurance despite the existing obstacles to risk sharing. Our approach can be applied in many contexts to foster financial inclusion, alleviate poverty traps, and complement existing safety net policies and mechanisms in a cost-effective way. We provide blueprints for design and implementation.

We solve and structurally estimate a range of dynamic models of international capital flows and risk sharing with imperfect capital markets. We study both settings with exogenously incomplete markets (debt with frictions in borrowing or capital outflows, non-defaultable debt) and endogenously incomplete markets (defaultable debt, limited commitment). All models share common preference and technology structure. We use computational methods based on mechanism design, linear programming and maximum likelihood to estimate and statistically test across the models to find which model fits the data best. Our methods work with either cross-sectional or panel data (on income, investment, capital, consumption, or all together) and allow for measurement error and unobserved heterogeneity. We use data on GDP, government expenditure, consumption, capital stock and investment per capita for 175 countries in 1993-2001. We find that, overall, the defaultable debt and autarky models fit the data best. The limited commitment and complete markets models are rejected in all specifications.

We study a network-based model of criminal activity. Agents' payoffs depend on the number and structure of links among them and are determined in a Nash equilibrium of a crime effort supply game. Unlike much of the existing literature that takes network structure as given, we analyze optimal network structures, defined as maximizing aggregate payoff. Using potential functions, we give necessary and sufficient conditions that guarantee the existence and uniqueness of equilibria with non-negativity constraints on effort. These results can be used to identify optimal networks for given cost and benefit parameter configurations drawing on graph theory and using a computational algorithm that searches over all possible non-isomorphic networks of a given size. Our results can be also used to study, via numerical simulations, the effects of alternative crime reducing policies on the network structure and crime level - removing agents, removing links or varying the probability of apprehension.

The paper presents a stylized general equilibrium model of a developing economy in which the wealth distribution, the interest rate, and the wage rate are endogenous and interact dynamically. A credit market imperfection stemming from limited commitment results in allocative inefficiency due to credit rationing and occupational choice constraints. Credit rationing is shown to persist as the economy develops. The proposed model is shown to match both general empirical regularities pertaining to developing economies and macroeconomic data from Thailand. Furthermore, wealth inequality in this setting may be detrimental for economic development, providing a rationale for redistribution policies.

This paper provides a step-by-step hands-on introduction to the techniques used in setting up and solving moral hazard programs with lotteries using Matlab. It uses a linear programming approach due to its relative simplicity and the high reliability of the available optimization algorithms.