Contributions in Merlin

We develop a novel geometric approach to mechanism design using an important result in convex analysis: the duality between a closed convex set and its support function. By deriving the support function for the set of feasible interim values we extend the wellknown Maskin-Riley-Matthews-Border conditions for reduced-form auctions to social choice environments. We next refine the support function to include incentive constraints using a geometric characterization of incentive compatibility. Borrowing results from majorization theory that date back to the work of Hardy, Littlewood, and Pólya (1929) we elucidate the "ironing" procedure introduced by Myerson (1981) and Mussa and Rosen (1978). The inclusion of Bayesian and dominant strategy incentive constraints result in the same support function, which establishes equivalence between these implementation concepts. Using Hotelling's lemma we next derive the optimal mechanism for any social choice problem and any linear objective, including revenue and surplus maximization. We extend the approach to include general concave objectives by providing a fixed-point condition characterizing the optimal mechanism. We generalize reduced-form implementation to environments with multi-dimensional, correlated types, non-linear utilities, and interdependent values. When value interdependencies are linear we are able to include incentive constraints into the support function and provide a condition when the second-best allocation is ex post incentive compatible.

In recent decades, the US has experienced a widening of the college enrolment gap between rich and poor families. This is commonly interpreted as evidence for a tightening of borrowing constraints. This paper asks whether this is indeed the case. I present an incomplete-markets overlapping-generations model with college enrolment, in which altruistic parents provide transfers to their children. In the model the rise in earnings inequality observed between 1980 and 2000 acts as the driving force for generating the trends in the data. With the help of counterfactual experiments, I find that fraction of constrained households is much higher (24 instead of 8 percent) than indicated by the narrow enrolment gap in 1980. Contrary to what the development of the enrolment gap in the data suggests, the share of constrained households actually fell (to 18 percent) between 1980 and 2000. I show that altruism is important for explaining these findings.

This study applies financial portfolio theory to determine efficient frontiers in the provision of electricity for the United States and Switzerland. Expected returns are defined by the rate of productivity increase of power generation (adjusted for external costs), volatility, by its standard deviation. Since unobserved productivity shocks are found to be correlated, Seemingly Unrelated Regression Estimation (SURE) is used to filter out the systematic component of the covariance matrix of the productivity changes. Results suggest that as of 2003, the feasible maximum expected return (MER) electricity portfolio for the United States contains more Coal, Nuclear, and Wind than actual but markedly less Gas and Oil. The minimum variance (MV) portfolio contains markedly more Oil, again more Coal, Nuclear, and Wind but almost no Gas. Regardless of the choice between MER and MV, U.S. utilities are found to lie substantially inside the efficient frontier. This is even more true of their Swiss counterparts, likely due to continuing regulation of electricity markets.

Many statistical applications require an estimate of a covariance matrix and/or its inverse. When the matrix dimension is large compared to the sample size, which happens frequently, the sample covariance matrix is known to perform poorly and may suffer from ill-conditioning. There already exists an extensive literature concerning improved estimators in such situations. In the absence of further knowledge about the structure of the true covariance matrix, the most successful approach so far, arguably, has been shrinkage estimation. Shrinking the sample covariance matrix to a multiple of the identity, by taking a weighted average of the two, turns out to be equivalent to linearly shrinking the sample eigenvalues to their grand mean, while retaining the sample eigenvectors. Our paper extends this approach by considering nonlinear transformations of the sample eigenvalues. We show how to construct an estimator that is asymptotically equivalent to an oracle estimator suggested in previous work. As demonstrated in extensive Monte Carlo simulations, the resulting bona fide estimator can result in sizeable improvements over the sample covariance matrix and also over linear shrinkage.

We study the effect of civil conflict on social capital, focusing on the experience of Uganda during the last decade. Using individual and county-level data, we document causal effects on trust and ethnic identity of an exogenous outburst of ethnic conflicts in 2002-04. We exploit two waves of survey data from Afrobarometer 2000 and 2008, including information on socioeconomic characteristics at the individual level, and geo-referenced measures of fighting events from ACLED. Our identification strategy exploits variations in the intensity of fighting both in the spatial and cross-ethnic dimensions. We find that more intense fighting decreases generalized trust and increases ethnic identity. The effects are quantitatively large and robust to a number of control variables, alternative measures of violence, and different statistical techniques involving ethnic and county fixed effects and instrumental variables. We also document that the post-war effects of ethnic violence depend on the ethnic fractionalization. Fighting has a negative effect on the economic situation in highly fractionalized counties, but has no effect in less fractionalized counties. Our findings are consistent with the existence of a self-reinforcing process between conflicts and ethnic cleavages.

In this paper, we quantitatively analyze to what extent a benevolent government should issue debt in a model where households are subject to idiosyncratic productivity shocks, insurance markets are missing and borrowing is restricted. In this environment, issuing government bonds facilitates saving for self-insurance. Despite this, we find that in a calibrated version of the model that is consistent with the skewed wealth and earnings distribution observable in the U.S., the government should buy private bonds, and not issue public debt in the long run. The reason is that in the U.S., a large fraction of the population has almost no wealth or is even in debt. The wealth-poor, however, do not profit from an increase in the interest rate following an increase in public debt. Instead, they gain from higher wages that result from a reduction in debt. We show that even when the short run costs of higher capital taxation are taken into account, it still pays off to reduce government debt on overall. Moreover, we find that endogenizing household’s borrowing constraints by assuming limited commitment leads to even higher asset levels being optimal in the long run.

A strategy profile of a game is called robustly stochastically stable if it is stochastically stable for a given behavioral model independently of the specification of revision opportunities and tie-breaking assumptions in the dynamics. We provide a simple radius-coradius result for robust stochastic stability and examine several applications. For the logit-response dynamics, the selection of potential maximizers is robust for the subclass of supermodular symmetric binary-action games. For the mistakes model, the weaker property of strategic complementarity suffices for robustness in this class of games. We also investigate the robustness of the selection of risk-dominant strategies in coordination games under best-reply and the selection of Walrasian strategies in aggregative games under imitation.

This paper studies the effect of leadership on the level and evolution of pro-social behavior using an artefactual field experiment on local public good provision. Participants decide how much to contribute to an actual conservation project. They can then revise their donations after being randomly matched in pairs on the basis of their authority and having observed each other’s contributions. Authority is measured through a social ranking exercise identifying formal and moral leaders within the community. I find that giving by a pair is higher and shows a lower tendency to decrease over time when a leader is part of a pair. This is because higher-ranked pair members in general, and leaders in particular, donate more and are less likely to revise contributions downwards after giving more than their counterparts. Leadership effects are stronger when moral authority is made salient within the experiment, in line with the ethical nature of the decision under study. These findings highlight the importance of identifying different forms of leadership and targeting the relevant leaders in projects aimed at local public good provision.

Many statistical applications require the forecast of a random variable of interest over several periods into the future. The sequence of individual forecasts, one period at a time, is called a path forecast, where the term path refers to the sequence of individual future realizations of the random variable. The problem of constructing a corresponding joint prediction region has been rather neglected in the literature so far: such a region is supposed to contain the entire future path with a prespecified probability. We develop bootstrap methods to construct joint prediction regions. The resulting regions are proven to be asymptotically consistent under a mild high-level assumption. We compare the finitesample performance of our joint prediction regions to some previous proposals via Monte Carlo simulations. An empirical application to a real data set is also provided.