We estimate the impact of mask mandates and other non-pharmaceutical interventions (NPI) on COVID-19 case growth in Canada, including regulations on businesses and gatherings, school closures, travel and self-isolation, and long-term care homes. We partially account for behavioral responses using Google mobility data. Our identification strategy exploits variation in the timing of indoor face mask mandates staggered over two months in the 34 public health regions in Ontario, Canada's most populous province. We find that mask mandates are associated with 25 percent or larger weekly reduction in new COVID-19 cases in July and August, relative to the trend in absence of mask mandate. Additional analysis with province-level data provides corroborating evidence. Counterfactual policy simulations suggest that mandating indoor masks nationwide in early July could have reduced the number of new cases in Canada by 25 to 40 percent in mid-August, which translates into 700 to 1,100 fewer cases per week.

I construct a dynamic social-network model of the COVID-19 epidemic which embeds the SIR epidemiological model onto a graph of person-to-person interactions. The standard SIR framework assumes uniform mixing of infectious persons in the population. This abstracts from important elements of realism and locality: (i) people are more likely to interact with members of their social networks and (ii) health and economic policies can affect differentially the rate of viral transmission via a person's social network vs. the population as a whole. The proposed network-augmented (NSIR) model allows the evaluation, via simulations, of (i) health and economic policies and outcomes for all or subset of the population: lockdown/distancing, herd immunity, testing, contact tracing; (ii) behavioral responses and/or imposing or lifting policies at specific times or conditional on observed states. I find that viral transmission over a network-connected population can proceed slower and reach lower peak than transmission via uniform mixing. Network connections introduce uncertainty and path dependence in the epidemic dynamics, with a significant role for bridge links and superspreaders. Testing and contact tracing are more effective in the network model. If lifted early, distancing policies mostly shift the infection peak into the future, with associated economic costs. Delayed or intermittent interventions or endogenous behavioral responses generate a multi-peaked infection curve, a form of `curve flattening', but may have costlier economic consequences by prolonging the epidemic duration.

I map the link between financial contracts and the algorithmic tools and constraints of blockchain technology related to property rights, information, commitment and enforcement. I explore the use of blockchains both as direct conduit of financial contracts in different incomplete market scenarios and also as collateral mechanism for on- and off-blockchain transactions and contracts.

We build and structurally estimate a model of occupational choice between entrepreneurship and wage work. We explicitly distinguish involuntary entrepreneurship (running a business out of necessity) from running a business by choice. Involuntary entrepreneurs are agents who would earn higher income in wage work but cannot obtain a job due to a labor market friction. We estimate the model via the simulated method of moments using Thai urban data. Our results imply a 19% fraction of involuntary entrepreneurs among all businesses. Involuntary entrepreneurs earn much lower income (80% less) than the voluntary and are more likely among low-wealth and low-schooling households. We also quantify and distinguish the misallocations in occupational choice and investment resulting from labor and credit market frictions. Our results imply 17% excess (involuntary) entrepreneurs relative to the first best because of the labor market friction and 1% less entrepreneurs because of the credit friction. Evaluating counterfactuals using the estimated model shows that reducing the credit constraint has only minor impact on occupational misallocations but policies that relax either the labor or credit constraints (e.g., access to microcredit) yield sizable income gains for the poor.

I construct a dynamic social-network model of the COVID-19 epidemic which embeds the SIR epidemiological model onto a graph of person-to-person interactions. The standard SIR framework assumes uniform mixing of infectious persons in the population. This abstracts from important elements of realism and locality: (i) people are more likely to interact with members of their social networks and (ii) health and economic policies can affect differentially the rate of viral transmission via a person's social network vs. the population as a whole. The proposed network-augmented (NSIR) model allows the evaluation, via simulations, of (i) health and economic policies and outcomes for all or subset of the population: lockdown/distancing, herd immunity, testing, contact tracing; (ii) behavioral responses and/or imposing or lifting policies at specific times or conditional on observed states. I find that viral transmission over a network-connected population can proceed slower and reach lower peak than transmission via uniform mixing. Network connections introduce uncertainty and path dependence in the epidemic dynamics, with a significant role for bridge links and superspreaders. Testing and contact tracing are more effective in the network model. If lifted early, distancing policies mostly shift the infection peak into the future, with associated economic costs. Delayed or intermittent interventions or endogenous behavioral responses generate a multi-peaked infection curve, a form of `curve flattening', but may have costlier economic consequences by prolonging the epidemic duration.

We build and structurally estimate a model of occupational choice between entrepreneurship and wage work. We explicitly distinguish involuntary entrepreneurship (running a business out of necessity) from running a business by choice. Involuntary entrepreneurs are agents who would earn higher income in wage work but cannot obtain a job due to a labor market friction. We estimate the model via the simulated method of moments using Thai urban data. Our results imply a 19% fraction of involuntary entrepreneurs among all businesses. Involuntary entrepreneurs earn much lower income (80% less) than the voluntary and are more likely among low-wealth and low-schooling households. We also quantify and distinguish the misallocations in occupational choice and investment resulting from labor and credit market frictions. Our results imply 17% excess (involuntary) entrepreneurs relative to the first best because of the labor market friction and 1% less entrepreneurs because of the credit friction. Evaluating counterfactuals using the estimated model shows that reducing the credit constraint has only minor impact on occupational misallocations but policies that relax either the labor or credit constraints (e.g., access to microcredit) yield sizable income gains for the poor.

We study the determinants and dynamics of transaction fees in the Ethereum blockchain platform. Transaction fees are endogenous prices for service measured in units of "gas" and paid when a direct transfer or smart-contract transaction is recorded on the blockchain. We estimate an empirical model based on queueing theory and analyze the factors determining Ethereum transaction fees. Using detailed block-level and transaction-level data downloaded directly from the Ethereum blockchain, we show that changes in service demand significantly affect the transaction fee level: when there is high blockchain utilization, transaction fees go up on average, in a non-linear way. Confounding supply side effects are offset by the blockchain algorithm, which by design maintains constant service capacity. We further establish that transaction type plays an important role in the fee determination. Categorizing the blockchain transactions into regular transactions (direct transfers) or contract calls (automated transactions), we find that a larger fraction of regular transactions is associated with higher transaction fees on average. These results are robust to different blockchain network conditions, time periods, and model specifications.

I map the link between financial contracts and the algorithmic tools and constraints of blockchain technology related to property rights, information, commitment and enforcement. I explore the use of blockchains both as direct conduit of financial contracts in different incomplete market scenarios and also as collateral mechanism for on- and off-blockchain transactions and contracts.

We formulate and solve a range of dynamic models of international capital flows and risk sharing with imperfect capital markets. We feature both models of exogenously incomplete markets (debt with tax on borrowing or on capital outflows, non-defaultable debt) and models with endogenously incomplete markets (defaultable debt, limited commitment), as well as the complete markets benchmark. All models share common preferences and technology. We use computational methods based on mechanism design, linear programming, and maximum likelihood to estimate and statistically test across the alternative models of international capital markets. Our methods work with cross-sectional or panel data and allow for measurement error and unobserved heterogeneity. We study which models fit best and also what type of data (income, investment, capital, consumption, or all together) can be used to distinguish across the alternative models. Empirically, we use panel data on GDP, government expenditure, consumption, capital stock and investment per capita for 175 countries in 1993-2002. We find that, overall, the defaultable debt and autarky models fit the data best. The complete markets and limited commitment models are rejected in all estimation runs.

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.