We develop a model in which the capital of the intermediary sector plays a critical role in determining asset prices. The model is cast within a dynamic general equilibrium economy, and the role for intermediation is derived endogenously based on optimal contracting considerations. Low intermediary capital reduces the risk-bearing capacity of the marginal investor. We show how this force helps to explain patterns during financial crises. The model replicates the observed rise during crises in Sharpe ratios, conditional volatility, correlation in price movements of assets held by the intermediary sector, and fall in riskless interest rates.

We study a novel dynamic principal–agent setting with moral hazard and adverse selection (persistent as well as repeated). In the model, an agent whose skills are his private information faces a finite sequence of tasks, one after the other. Upon arrival of each task, the agent learns its level of difficulty and then chooses whether to accept or refuse each task in turn and how much effort to exert. Although his decision to accept or refuse a task is publicly known, the agent's effort level is his private information. We characterize optimal contracts and show that the per-period utility of the agent approaches his per-period utility when his skills are publicly known, as the discount factor and the time horizon increase.

The optimal design of low-income support is examined using a structural labour supply model. The approach incorporates unobserved heterogeneity, fixed costs of work, childcare costs and the detailed non-convexities of the tax and transfer system. The analysis considers purely Pareto improving reforms and also optimal design under social welfare functions with different degrees of inequality aversion. We explore the gains from tagging and also examine the case for the use of hours-contingent payments. Using the tax schedule for lone parents in the U.K. as our policy environment, the results point to a reformed non-linear tax schedule with tax credits only optimal for low earners. The results also suggest a welfare improving role for tagging according to child age and for hours-contingent payments, although the case for the latter is mitigated when hours cannot be monitored or recorded accurately by the tax authorities.

We develop a dynamic theory of resource wars and study the conditions under which such wars can be prevented. Our focus is on the interaction between the scarcity of resources and the incentives for war in the presence of limited commitment. We show that a key parameter determining the incentives for war is the elasticity of demand. Our first result identifies a novel externality that can precipitate war: price-taking firms fail to internalize the impact of their extraction on military action. In the case of inelastic resource demand, war incentives increase over time and war may become inevitable. Our second result shows that in some situations, regulation of prices and quantities by the resource-rich country can prevent war, and when this is the case, there will also be slower resource extraction than the Hotelling benchmark (with inelastic demand). Our third result is that because of limited commitment and its implications for armament incentives, regulation of prices and quantities might actually precipitate war even in some circumstances where wars would not have arisen under competitive markets.

Counterparty credit risk has become one of the highest-profile risks facing participants in the financial markets. Despite this, relatively little is known about how counterparty credit risk is actually priced. We examine this issue using an extensive proprietary data set of contemporaneous CDS transaction prices and quotes by 14 different CDS dealers selling credit protection on the same underlying firm. This unique cross-sectional data set allows us to identify directly how dealers' credit risk affects the prices of these controversial credit derivatives. We find that counterparty credit risk is priced in the CDS market. The magnitude of the effect, however, is vanishingly small and is consistent with a market structure in which participants require collateralization of swap liabilities by counterparties.

[Lifetime incomes of private equity general partners (GPs) are affected by their current funds' performance not only directly, through carried interest profit-sharing provisions, but also indirectly by the effect of the current fund's performance on GPs' abilities to raise capital for future funds. In the context of a rational learning model, which we show better matches the empirical relations between future fund-raising and current performance than behavioral alternatives, we estimate that indirect pay for performance from future fund-raising is of the same order of magnitude as direct pay for performance from carried interest. Consistent with the learning framework, indirect pay for performance is stronger when managerial abilities are more scalable and weaker when current performance is less informative about ability. Specifically, it is stronger for buyout funds than for venture capital funds, and declines in the sequence of a partnership's funds. Total pay for performance in private equity is both considerably larger and much more heterogeneous than implied by the carried interest alone. Our framework can be adapted to estimate indirect pay for performance in other asset management settings.]

We estimate the effects of interstate highways on the growth of U.S. cities between 1983 and 2003. We find that a 10% increase in a city's initial stock of highways causes about a 1.5% increase in its employment over this 20 year period. To estimate a structural model of urban growth and transportation, we rely on an instrumental variables estimation that uses a 1947 plan of the interstate highway system, an 1898 map of railroads, and maps of the early explorations of the U.S. as instruments for 1983 highways.

We develop results for the use of LASSO and Post-LASSO methods to form firststage predictions and estimate optimal instruments in linear instrumental variables (IV) models with many instruments, p, that apply even when p is much larger than the sample size, n.We rigorously develop asymptotic distribution and inference theory for the resulting IV estimators and provide conditions under which these estimators are asymptotically oracle-efficient.In simulation experiments, the LASSO-based IV estimator with a data-driven penalty performs well compared to recently advocated many-instrument-robust procedures.In an empirical example dealing with the effect of judicial eminent domain decisions on economic outcomes, the LASSObased IV estimator substantially reduces estimated standard errors allowing one to draw much more precise conclusions about the economic effects of these decisions.Optimal instruments are conditional expectations; and in developing the IV results, we also establish a series of new results for LASSO and Post-LASSO estimators of non-parametric conditional expectation functions which are of independent theoretical and practical interest.Specifically, we develop the asymptotic theory for these estimators that allows for non-Gaussian, heteroscedastic disturbances, which is important for econometric applications.By innovatively using moderate deviation theory for self-normalized sums, we provide convergence rates for these estimators that are as sharp as in the homoscedastic Gaussian case under the weak condition that log p = o(n 1/3 ).Moreover, as a practical innovation, we provide a fully data-driven method for choosing the user-specified penalty that must be provided in obtaining LASSO and Post-LASSO estimates and establish its asymptotic validity under non-Gaussian, heteroscedastic disturbances.

The paper analyzes the effect of competition between credit rating agencies (CRAs) on the information content of ratings. We show that a monopolistic CRA pools sellers into multiple rating classes and has partial market coverage. This provides an opportunity for market entry. The entrant designs a rating scale distinct from that of the incumbent. It targets higher-than-average companies in each rating grade of the incumbent's rating scale and employs more stringent rating standards. We use Standard and Poor's (S&P) entry into the market for insurance ratings previously covered by a monopolist, A.M. Best, to empirically test the impact of entry on the information content of ratings. The empirical analysis reveals that S&P required higher standards to assign a rating similar to the one assigned by A.M. Best and that higher-than-average quality insurers in each rating category of A.M. Best chose to receive a second rating from S&P.